Abstract
Object detection is an important research field of computer vision, but getting accurate object detection from a large number of detection candidates has always been a challenge. The most current algorithms use an insufficient Greedy Non-Maximum Suppression (NMS) strategy which heavily relies on the confidence of the detection candidates. This paper proposes the Iterative Detection Filter (IDF) approach, which considers more information of the detection candidates, including overlapping, the confidence generated by the detector, and the ground position perception information of the scene. Through this approach, the detection candidates are mapped to more accurate detections. Our method achieves a significant improvement on the MOT16 and MOT17 datasets, which are widely used in video tracking and detection.
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Acknowledgement
This study is partially supported by the National Key R & D Program of China (No. 2016QY01W0200), the National Natural Science Foundation of China (No. 61472019), the Macao Science and Technology Development Fund (No. 138/2 016/A3), the Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09, the Project of Experimental Verification of the Basic Commonness and Key Technical Standards of the Industrial Internet network architecture, and the Technology Innovation Fund of China Electronic Technology Group Corporation. Thank you for the support from HAWKEYE Group.
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Zhang, X. et al. (2018). Iterative Maximum Clique Clustering Based Detection Filter. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_13
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