Based On Deep Networks, Video Object Detection Is Actively Studied For Pushing The Limits Of Detection Speed And Accuracy.
This paper proposes a vanilla dynamic aggregation module that adaptively selects the frames for feature enhancement and extends it to a more effective and reconfigurable. To reduce the computation cost, we sparsely sample. It is a part of the.
In This Paper, We Build Our Video Object Detector.
One practical solution is to take advantage of temporal information from the video and apply feature aggregation to enhance the object features in each frame. Dynamic feature aggregation for efficient video object detection | video object detection is a fundamental yet challenging task in computer. One practical solution is to take advantage of te.
For Example, In Frame T + 3 S Of Fig.
The proposed feature alignment module is a correlation based feature alignment method to align the support and target frames for feature aggregation in the temporal domain. Efficient unsupervised video object segmentation network based on motion guidance. This paper proposes a vanilla dynamic aggregation module that adaptively selects the frames for feature enhancement and extends it to a more effective and reconfigurable.
Video Object Detection Is A Fundamental Yet Challenging Task In Computer Vision.
In comparison with static image object detection, focusing on video objects has greater research significance in realizing intelligent monitoring and automatic anomaly. One practical solution is to take advantage of temporal information from the video and apply feature aggregation to enhance the object features in each frame. 1 (b), the cat and fox become blurred after a sudden moving, which may introduce additional noise to frame t.
Dynamic Feature Aggregation For Efficient Video Object Detection Introduction.
Mmtracking is an open source video perception toolbox based on pytorch. No code yet • 10 nov 2022 then, the semantic features of the motion. Objects in videos tend to be blurred, occluded, or out of focus more.