3250 results about "Motion vector" patented technology

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

A dynamic adaptive depth camera occlusion detection method, system and device, and a storage medium, the method comprising: step T1: constructing a three-frame time sequence analysis window, continuously obtaining infrared speckle image sequences at different moments and corresponding dense depth map sequences, and establishing a pixel-level space-time alignment mapping relationship; t2, calculating a motion vector between adjacent frames, and executing the step T3 when the average motion intensity exceeds a dynamic threshold value; wherein the dynamic threshold is determined by the dense depth map; t3, positioning a speckle feature point in the infrared speckle image, generating an analysis window by taking the speckle feature point as a center, calculating a gradient change rate, and when the gradient change rate is smaller than a gradient threshold value, marking the region as a primary candidate region; and T4, carrying out space-time consistency comparison on the primary candidate region and two adjacent frames, and reserving a shielding region of which the variation value is smaller than a variation threshold value in three continuous frames. The method is high in precision, good in real-time performance and high in stability.

The invention discloses a system for improving the applicability of an intelligent camera, and relates to the technical field of intelligent camera shooting. RGB, infrared and depth sensors are used for cooperative sensing, a dynamic inter-frame interpolation algorithm is used for complementing video data, target trajectory prediction is carried out based on a target motion vector analysis algorithm in combination with a recurrent neural network LSTM + Kalman filtering, and the applicability of the intelligent camera is improved. Meanwhile, three-dimensional scene modeling and shielding recovery are introduced, and environment information complementation of a dead angle area is optimized; through a cross-camera target identity re-identification ReID algorithm, seamless tracking is carried out among multiple cameras by utilizing appearance characteristics, gait characteristics and biological characteristics of a target, and meanwhile, PTZ camera control is optimized in combination with reinforcement learning, and the angles of the cameras are dynamically adjusted; a generative adversarial network GAN based on space-time consistency is adopted, historical video data and environment modeling are combined, missing pictures in visual dead angles are intelligently complemented, and the continuity and authenticity of monitoring pictures are improved.

The invention provides a lightweight AI personnel perception method and system based on a millimeter wave radar, and relates to the technical field of personnel perception, and the method comprises the steps: carrying out the phase compensation, amplitude calibration and bidirectional combined filtering processing of a millimeter wave echo signal, and constructing three-dimensional space point cloud data. Then, constructing a spatial distribution matrix based on the point cloud data, calculating the center position of a point cloud cluster, establishing an adaptive sampling radius to extract point cloud features, and calculating a motion vector and attitude parameters; and finally, inputting the motion vector and the attitude parameter into a double threshold judgment module, judging whether a person exists in the target area, generating a person position thermodynamic diagram based on the center position of the point cloud cluster, and outputting a person perception result. According to the method, the calculation complexity is reduced, the real-time performance and accuracy of personnel perception are improved, and the method is suitable for smart home, security monitoring and other scenes.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention discloses a security and protection monitoring video real-time transmission method based on the Internet of Things, and relates to the technical field of security and protection monitoring video transmission, and the method specifically comprises the following steps: when a texture repetition region exists in an image frame, determining all macro blocks forming the texture repetition region in the image frame, and marking the macro blocks as texture repetition blocks; performing comprehensive analysis on each texture repetition block, and evaluating a candidate motion vector direction dispersion degree when an image frame in the security and protection monitoring video has a texture repetition area; based on the evaluation result, determining whether to classify each texture repetition block and whether to respectively match a corresponding motion vector selection strategy; and according to the matched motion vector selection strategy, respectively executing corresponding dynamic regulation and control operations. According to the method, the problem that the direction of the motion vector is discrete and uncontrollable in a texture repetition area is solved, dynamic regulation and control of a coding strategy and synchronous optimization of a decoding end are realized, and the image reconstruction precision and the intelligent analysis stability are improved.

Disclosed in the present invention are a UWB-based positioning apparatus and positioning method for a point target of a pointing apparatus. The positioning apparatus comprises a pointing apparatus and a receiving apparatus, wherein UWB antennas are respectively arranged on the pointing apparatus and the receiving apparatus, and an accelerometer and a gyroscope are further arranged on the pointing apparatus. By means of the pointing apparatus and the receiving apparatus, UWB-based positioning is performed to obtain coordinates of the pointing apparatus in a coordinate system in which the receiving apparatus is located; then by means of the accelerometer, a motion vector of the pointing apparatus is obtained, such that a linear equation for a corresponding pointing direction of the pointing apparatus is obtained; and by means of solving an intersection point of the linear equation and a pointing area, coordinates of a pointing point are determined, thereby achieving a design purpose.

The invention discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention relates to the technical field of image optimization recognition, in particular to a deep foundation pit crack detection method and device for deep foundation pit tunneling. According to the method, a deep foundation pit image and a motion vector of an image acquisition device are obtained; and blurred images in the deep foundation pit images are screened out, and rigid edges are identified. And screening out the smear edge by using the position relationship between other edges and the rigid edge, the pixel value change stability on the path and the correlation degree between the edge vector and the motion vector. And constructing a two-dimensional fuzzy kernel, and adaptively setting a fuzzy kernel value by using gradient information and position information of pixel values in the two-dimensional fuzzy kernel. And performing deblurring on the blurred image through the two-dimensional blurring kernel, and performing crack identification. The feature information in the blurred image is extracted and analyzed, the smear effect in the image is quantified, the blurred kernel in the image is determined in a self-adaptive mode, deblurring is carried out, the quality of the deep foundation pit image is improved, and the accuracy of a crack recognition result is improved.

The invention relates to the technical field of solar cell processing, in particular to a laser light-transmitting scribing control method and system for a solar thin-film cell. The control method comprises the steps that a large-size substrate is divided into a plurality of galvanometer view fields through a dynamic flight continuous machining mode, a continuous snake-shaped machining path is generated, the laser machining platform is driven to continuously move at the constant speed, and the galvanometer scanning system is synchronously triggered to start lineation in a splicing area in advance. According to the method, a time domain coupling equation of galvanometer scanning frequency-platform movement speed-laser pulse sequence is established, and dynamic compensation of an optical path and self-adaptive adjustment of energy density are achieved in the continuous machining process. Based on a view field expansion algorithm of a nonlinear kinematics model, the phase deviation of a scanning vector and a motion vector is effectively inhibited, and the geometric continuity of a splicing interface in ultra-long stroke machining is guaranteed.

The invention relates to the technical field of image target tracking, and discloses a video image target tracking method and processing device based on multi-feature fusion, and the method comprises the steps: obtaining an initial position parameter and a current frame detection parameter of a target; calculating according to the initial position parameter to obtain a pixel-level motion vector field; performing prediction according to the pixel-level motion vector field to obtain a prediction position parameter of the target; comparing the predicted position parameter with the current frame detection parameter, and when the difference between the predicted position parameter and the current frame detection parameter is smaller than a preset threshold value, judging that the target is not shielded; and when the difference between the predicted position parameter and the current frame detection parameter is greater than a preset threshold value, tracking error accumulation may be caused by too early view angle switching in the prior art, and in a resource-limited scene, the calculation overhead of a deep learning model is generally large, resulting in poor tracking accuracy and real-time performance.

Disclosed herein is a method of transmitting point cloud data. The method may include encoding geometry data of the point cloud data, encoding attribute data of the point cloud data based on the geometry data, and transmitting the encoded geometry data, the encoded attribute data and signaling data, the geometry encoding includes splitting the geometry data into one or more prediction units, and inter-prediction encoding the geometry data by selectively applying a motion vector to each of the split prediction units, and the signaling data includes information for identifying whether the motion vector is applied for each prediction unit.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

The invention discloses an extreme compressed video coding method based on an intelligent reference frame, comprising the following steps: S1, acquiring an original video stream, dividing the original video stream into frame groups according to a time sequence, each group comprising a current frame and a candidate reference frame; s2, carrying out region positioning on the candidate reference frame, extracting motion, edge and background regions, and generating a mapping graph; s3, calculating a quality score based on the mapping graph according to a motion vector, gradient change and a region overlapping rate; s4, selecting three frames with highest scores to form a reference set, and establishing an index structure; s5, predicting the current frame by using the reference set to generate a predicted frame and a residual error; s6, multi-path coding cost is calculated, and a path with the minimum cost is selected; and S7, entropy coding is carried out on the reference index, the motion vector, the residual error and the control parameter, a code stream is output, and calling information is recorded. According to the method, the compression ratio and prediction precision of video coding are improved, the image quality is maintained while the code rate is reduced, and the method is suitable for efficient transmission and storage of high-resolution videos.

The invention discloses a dynamic object suppression video image stabilization method based on depth information and global consistency, and the method comprises the steps: extracting key points in a video, carrying out the optical flow estimation, and obtaining the depth information of the key points; a static weight and a motion vector residual error are calculated based on the motion information, the depth information and the global homography matrix of each key point, a dynamic weight is obtained based on an attention mechanism, the dynamic weight and the static weight are fused to obtain a comprehensive weight, and a weighted motion residual error is calculated based on the comprehensive weight and the motion vector residual error; spreading the motion information to grid vertexes, obtaining dense residual motion based on weighted motion residual prediction, and accumulating the dense residual motion to obtain a track; and carrying out smoothing and motion compensation on the track and outputting a stable video sequence. The problem that in a complex scene, motion of a dynamic object causes distortion and inaccuracy of a stabilization result is solved, interference of the dynamic object can be effectively suppressed, and the effect and robustness of video image stabilization are improved.

The invention discloses a multistage video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the perception analysis of an original video through a space-time attention neural network, and dynamically dividing a time domain stable region and a space domain sensitive region; a three-level watermark system is constructed, the three-level watermark system comprises a basic-level watermark, a first-level enhanced watermark and a second-level enhanced watermark, the basic-level watermark adopts an anti-rotation binary matrix generated by a Fourier descriptor, the first-level enhanced watermark is a DCT frequency domain Hash sequence, and the second-level enhanced watermark is a semantic fingerprint generated based on a motion vector. According to the method, through multi-dimensional collaborative optimization of the anti-attack capability, the dynamic security mechanism and the verification efficiency, the core defects that erasing is easy due to insufficient reversibility in a traditional video watermarking technology and counterfeit affiliation chaos is caused by a static key are overcome, and reliable technical guarantee is provided for digital content copyright protection.

This disclosure relates to method and system for inspection of rotational components based on video frames having different camera views of defects on rotational components. Using optical flow to ascertain motion vectors of pixels of the video frames, the video frames are partitioned based on motion vectors. The partitioned regions are paired with corresponding regions in a subsequent video frame, and their features are matched. For video frames having at least camera motion, a transformation matrix is ascertained which is applied to map defect trajectories of each camera view to a subsequent camera view.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention discloses a light rail vehicle safety monitoring system based on multi-sensor fusion, and relates to the technical field of light rail vehicle safety monitoring, the system is composed of a plurality of functional modules, and the system comprises a multi-mode sensing module for capturing images of the surrounding environment of a light rail in real time and delimiting a visual mark area; performing three-dimensional scanning on the visual marking area, generating environment point cloud data in real time, and acquiring a dynamic object distance by using a Doppler compensation algorithm; the data processing module is used for calculating object deformation between adjacent frames by adopting an optical flow method and a CNN deformation detection network; carrying out object relative motion calculation according to object shape analysis; carrying out instance segmentation based on MaskR-CNN, and outputting an object contour; meanwhile, a dynamic target clustering algorithm is used for conducting multi-target tracking on the object contour, and three-dimensional coordinates and motion vectors of the object are output in combination with environment point cloud data; and the fusion and analysis module performs time synchronization based on the PTP protocol.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

According to a present disclosure, an image decoding method performed by a decoding apparatus, the image decoding method comprising obtaining image information including inter prediction-related information of a current block from a bitstream; configuring at least one of a merge list of the current block and an advanced motion vector prediction (AMVP) list of the current block for inter prediction; deriving motion information of the current block on the basis of the at least one of the merge list of the current block and the AMVP list of the current block; generating prediction samples for the current block based on the motion information of the current block; and generating reconstruction samples for the current block based on the prediction samples for the current block, wherein the merge list includes at least one of a primary merge list and a secondary merge list (SML).

The invention relates to the technical field of liquid crystal display, in particular to a liquid crystal display driving control method and system. Comprising the following steps: SS01, multi-modal sensing and state triggering: fusing a user distance, a fixation point coordinate and ambient light illumination in real time by using an integrated sensor, and triggering an eye protection mode or a self-adaptive sleep mechanism according to the user distance, the fixation point coordinate and the ambient light illumination, SS02, image partitioning and strategy generation: dividing a screen into N * M partitions, and SS03, distinguishing a dynamic region from a static region according to the brightness histogram and the motion vector of each partition, and outputting differentiated refresh rates and backlight strategies, and SS03, performing big data learning and model optimization, and collecting user behaviors. The system has the advantages that the infrared proximity sensor, the eyeball tracking sensor, the distance sensor and the ambient light sensor are integrated through the multi-mode sensing and state triggering module, multi-dimensional data such as user distance, fixation point coordinates and ambient light illuminance are fused in real time, and active intelligent sensing of the user state and the environment is achieved.

The invention belongs to the technical field of signal synchronization, and discloses a multi-camera anti-shake time sequence synchronization control system and method based on an FPGA. The method comprises the following steps: taking an FPGA as a core, generating an FSYNC signal as a camera exposure reference, and obtaining a delay value through a timestamp and differential measurement; acquiring I MU data and obtaining a global motion vector through a gradient descent algorithm; analyzing by combining the two to obtain phase deviation and feeding back the phase deviation to the FPGA, and adjusting the FSYNC signal by combining the global motion vector by the FPGA according to the phase deviation; according to the method, nanosecond timestamp recording is realized through a homologous clock reference and hardware-level signal binding mechanism, and the limitation of dependence on static time difference compensation in the prior art is broken through in combination with global motion vector analysis and dynamic phase offset adjustment.

The invention relates to the technical field of image recognition, in particular to an AI-based park intrusion detection and response system, which comprises a target trajectory acquisition module, an event density analysis module, a disturbance behavior recognition module and a response joint control execution module. According to the invention, through fusion of the spatial-temporal characteristics of the target trajectory and motion vector pattern recognition, intrusion behavior dynamic risk assessment and early warning collaborative optimization is realized, and through combination of multi-dimensional data association calculation and event density increment monitoring, abnormal situation sensing sensitivity and discrimination accuracy in a complex scene are improved. Motion vector time sequence modeling and disturbance feature extraction are utilized, the capacity of capturing camouflage behaviors and irregular motion is enhanced, an environment perception and response execution closed-loop control link is constructed by means of intelligent matching and parameter self-adaptive adjustment of multi-source sensing equipment, the dependency degree of manual review is remarkably reduced, the delay from recognition to treatment response is shortened, and the recognition efficiency is improved. And an early warning research and judgment and linkage disposal full-process automation system is formed.

Systems and method are herein provided for motion vector calculation, processing, and filtering. In one example, a method comprises receiving a plurality of image frames as inputs; calculating one or more internal motion vector (MV) fields between a past frame (PF) and a current frame (CF) of the plurality of image frames; generating a foreground MV field and a background MV field of the one or more MV fields; processing the one or more internal MV fields and the foreground and background MV fields to generate one or more depth fields, wherein processing the one or more internal MV fields comprises generating virtual depths of the one or more internal MV fields to generate one or more MVD fields; and outputting the one or more MVD fields for image processing.

The invention discloses a vehicle positioning method and system, and relates to unmanned driving. According to IMU data and laser radar data, a fast-lio algorithm is used to construct a global prior map; constructing a local point cloud map according to laser radar data collected in real time; recognizing and rejecting dynamic point clouds, and carrying out NDT registration on residual static point clouds and the global prior map to obtain a laser odometer pose; extracting feature points of the camera data by adopting a shii-tomasi algorithm; a Lucas-Kanade optical flow method is used to acquire motion vectors of the feature points; calculating the speed of the feature points according to the motion vector, removing the dynamic feature points with the speed exceeding a preset threshold value, and obtaining the pose of the visual odometer; reckoning the position and course of the vehicle through a CTRV model to obtain a track reckoning result; and carrying out multi-source heterogeneous data fusion by adopting a non-destructive Kalman filtering algorithm. Aiming at the defects of dynamic environment interference and non-linear motion trail modeling existing in traditional open air and mines, the positioning stability of the unmanned vehicle is improved.

The invention provides a writing standard monitoring method and electronic equipment. The method comprises the steps that a dynamic contact state diagram changing along with a time sequence is generated; acquiring a continuous image sequence in the writing process by using a camera device at the upper end of the electronic equipment, and extracting pen point movement track characteristics and finger joint movement vectors; performing space-time alignment on the stroke sequence features decomposed from the continuous image sequence and the dynamic contact state diagram, and establishing a dynamic trajectory comparison model based on bimodal data fusion; performing multi-dimensional fusion on a space-time matching degree parameter output by the dynamic track comparison model and a pressure distribution parameter in the dynamic contact state diagram, and generating a continuously changing dynamic standard degree parameter through weighting; and when the local stroke segment deviates from the standard threshold value, activating the corresponding spectrum feedback signal according to the deviation type. According to the method, the robustness of track detection is improved through tactile-visual sensor fusion, and correlation analysis of writing elements is realized by means of space-time modeling.

The various implementations described herein include methods and systems for coding video. In one aspect, a method includes receiving a video bitstream comprising a current picture composed of a plurality of blocks, including a current block; identifying a reference block by applying a template-matching technique to the current block; identifying a reference vector for the reference block, where the reference vector is a motion vector or a block vector; populating a merge candidate list for the current block using the reference vector; and reconstructing the current block using information from the merge candidate list.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.