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

TL;DR

This paper introduces an adaptive patch-of-interest composition method that enhances object detection accuracy and speed by intelligently selecting and composing image patches, maintaining resolution while reducing input size.

Contribution

The paper proposes a novel adaptive patch composition technique that balances detection accuracy and speed by selecting and combining image patches for efficient object detection.

Findings

Improves detection accuracy by maintaining original resolution.

Reduces detection time by minimizing input patches.

Effective across multiple datasets.

Abstract

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.