Video Recognition via Key Frame Feature Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video recognition technologies face challenges with poor recognition performance and low speed due to issues like motion blur and out of focus in video frames, which are not adequately addressed by direct extensions of single-image recognition methods.
Innovation Solution
The method involves extracting features from key frames in a video, performing fusion with previous key frames using optical flow fields to enhance feature propagation and detection, and adjusting network parameters based on object detection results and tag information for improved recognition accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single-image recognition methods are directly extended to video recognition, then implementation simplicity is maintained, but recognition performance deteriorates due to motion blur and out of focus issues
Solution Approach 1:
The patent merges multiple key frames in a video sequence to perform recognition. Instead of recognizing a single image, the system extracts features from multiple key frames, fuses these features, and performs recognition on the fused result. This combining approach compensates for motion blur and focus issues in individual frames by leveraging information from multiple temporal instances, thereby improving recognition performance while maintaining a relatively streamlined implementation.
Solution Approach 2:
The patent performs preliminary feature extraction from multiple key frames before the actual recognition process. By pre-extracting and fusing features from multiple frames in advance, the system prepares enhanced input data that is more robust to motion blur and focus problems. This preliminary action of feature fusion before recognition improves the quality of input data without adding complexity to the core recognition algorithm.
2Measurement precision
If multiple key frames are processed for fusion to improve recognition accuracy, then recognition performance improves, but computation overhead increases
Solution Approach 1:
The patent segments the video into multiple key frames at specific time points rather than processing every frame. By selecting only key frames (e.g., frames at regular intervals or frames with significant changes), the system reduces the number of frames requiring feature extraction and fusion. This segmentation approach maintains recognition accuracy by capturing essential temporal information while significantly reducing computation overhead compared to processing all frames.
Solution Approach 2:
The patent applies partial action by processing only a subset of frames (key frames) rather than all frames in the video. This selective processing provides sufficient temporal information for accurate recognition without the excessive computational burden of full-frame processing. The key frame selection strategy ensures that the most informative frames are captured while minimizing redundant computation.
3Productivity
If feature fusion across multiple key frames is performed, then recognition speed improves by reducing redundant computation, but system complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction and fusion operations on key frames before the actual recognition step. By pre-processing and fusing features from multiple key frames in advance, the system creates enhanced input data that accelerates the subsequent recognition process. This preliminary action reduces redundant computation during recognition by providing pre-integrated temporal information, thereby improving recognition speed while organizing complexity into manageable preprocessing stages.
Data Source
AI summary
A method and an apparatus for recognizing and training a video, an electronic device and a storage medium include: extracting features of a first key frame in a video; performing fusion on the features of the first key frame and fusion features of a second key frame in the video to obtain fusion features of the first key frame, where a detection sequence of the second key frame in the video precedes that of the first key frame; and performing detection on the first key frame according to the fusion features of the first key frame to obtain an object detection result of the first key frame. Through iterative multi-frame feature fusion, information contained in shared features of these key frames in the video can be enhanced, thereby improving frame recognition accuracy and video recognition efficiency.