A robust learning-based detection and tracking algorithm

Dini Nuzulia Rahmah*, Wen-Huang Cheng, Yung Yao Chen, Kai Lung Hua

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Object tracking in video is a challenging problem in several applications such as video surveillance, video compression, video retrieval, and video editing. Tracking an object in a video is not easy due to loss of information caused by illumination changing in a scene, occlusions with other objects, similar target appearances, and inaccurate tracker responses. In this paper, we present a novel object detection and tracking algorithm via structured output prediction classifier. Given an initial bounding box with its position, we first divide it into sub-blocks with a predefined size. Next, we extract the features from each subblocks with Haar-like features method. And then we learn those features with a structured output prediction classifier. We treat the sub-blocks obtained from the initial bounding box as positive samples and then randomly choose negative samples from search windows defined by the specific area around the bounding box. After that, we obtain prediction scores for each sub-blocks both from positive and negative samples. We construct a region-graph with sub-blocks as nodes and classifier’s score as weight to detect the target object in each frame. Our experimental results show that the proposed method outperforms state-of-the-art object tracking algorithms.

Keywords

  • Object detection
  • Object tracking
  • Support vector machine

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