  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.
