Target detection and recognition method for image sequence

An image sequence and target detection technology, which is applied in image analysis, image data processing, character and pattern recognition, etc., can solve the problems of inability to detect with high precision, image quality degradation, and low target detection accuracy

Active Publication Date: 2019-01-11
JINLING INST OF TECH
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Problems solved by technology

[0011] In view of the fact that the current deep convolutional neural network target detection algorithm based on a single image cannot detect the target with high precision in the case of motion blur, image quality degradation, partial occlusion of the detection target, and large deformation of the dete

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  • Target detection and recognition method for image sequence
  • Target detection and recognition method for image sequence
  • Target detection and recognition method for image sequence

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Embodiment Construction

[0027] On the basis of the previous research, inspired by the multi-obstacle detection of autonomous driving, the single-image target detection algorithm represented by Faster R-CNN is extended to detect on video time-series images. The model architecture is as figure 2 As shown, the bottom layer is the image of each frame of the video. These images are respectively input into the shared convolutional network, and after calculating the convolutional feature map (Feature Map) of each frame, the convolutional feature map (Feature Map) of the current frame is then merged by the fusion algorithm ( Feature Map) and the convolutional feature map (Feature Map) of the front and rear multi-frames. Input the generated fused convolutional feature map (Feature Map) into the Region Proposal Network (Region Proposal Network), and the RPN (Region Proposal Network) network uses a sliding window to slide on the fused convolutional feature map (Feature Map) to generate a region recommendation ...

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Abstract

The invention belongs to the technical field of image detection and recognition, and can be used in the fields related to image sequence processing, such as automatic piloting, detection and recognition of visual objects of unmanned aerial vehicles, and detection of animation video. Firstly, the input image is transformed by Image Warping (Image Warping) based on motion compensation, then the depth convolution feature maps are extracted from the winding images by depth convolution neural network, and then the depth convolution feature maps of the image sequences are fused. Finally, the fused depth convolution feature maps are used in target detection modules such as target location, target recognition or region recommendation. A main technical feature of that invention comprises two partsof a computing module of a winding image based on motion compensation in the early stage and a fusion module of a depth convolution feature map of a front image sequence and a rear image sequence in the late stage. The invention can overcome the problem that the detection accuracy of a single image is low or even cannot be detected due to partial occlusion, motion blur and large deformation of thedetection target.

Description

technical field [0001] The invention belongs to the technical field of image detection and recognition, and in particular relates to a detection and recognition method for an image sequence target. Background technique [0002] In the field of image understanding, deep learning technology is widely used in image classification, image object detection, face recognition, etc. The network model represented by Faster R-CNN has achieved good recognition results in image object detection applications. In image and video applications, deep learning technology is widely used in the field of unmanned driving of cars, video surveillance, target detection and recognition of drones and other fields. Due to the partial occlusion of the target, the blur caused by too fast movement, and the large deformation of the target in the scene, it often leads to problems such as low detection accuracy or no detection at all. In animation video detection and recognition, due to the exaggeration of ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T7/269G06N3/04
CPCG06T7/269G06V20/40G06N3/045G06F18/253Y02T10/40
Inventor 龚如宾
Owner JINLING INST OF TECH
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