Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition

Shihao Zhang, Weiyao Lin, Ping Lu, Weihua Li, Shuo Deng

Object detection is an important yet challenging task in video understanding & analysis, where one major challenge lies in the proper balance between two contradictive factors: detection accuracy and detection speed. In this paper, we propose a new adaptive patch-of-interest composition approach for boosting both the accuracy and speed for object detection. The proposed approach first extracts patches in a video frame which have the potential to include objects-of-interest. Then, an adaptive composition process is introduced to compose the extracted patches into an optimal number of sub-frames for object detection. With this process, we are able to maintain the resolution of the original frame during object detection (for guaranteeing the accuracy), while minimizing the number of inputs in detection (for boosting the speed). Experimental results on various datasets demonstrate the effectiveness of the proposed approach.

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