상세정보
학술대회
Soft Assignment and Multiple Keypoint Analysis-based Pedestrian Counting Method
- 발행일
- 201810
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1479-1481
- 협약과제
- 18HS1800, 신체기능의 이상이나 저하를 극복하기 위한 휴먼 청각 및 근력 증강 원천 기술 개발, 신형철
- 초록
- Pedestrian counting in videos is an active computer vision research topic that has wide ranging application. Existing pedestrian counting methods predominantly use features extracted from the foreground following subtraction of the background. However, accurately locating the foreground in real environments is difficult, and background subtraction is computationally expensive. The keypoint approach, which counts pedestrians without background subtraction, is limited owing to lack of sufficient features and no consideration for stationary pedestrians. This letter proposes an accurate keypoint-based pedestrian counting method. As no single keypoint detector can yield optimal counting results under all conditions, such as image resolution, frame rate, and illumination, we combine complementary keypoint detectors to enrich the features and thereby enhance pedestrian counting results. In addition, the proposed method considers stationary pedestrians by analyzing static keypoints information. Information loss during vector quantization is also reduced by applying soft assignment during feature extraction. The results of experiments conducted on public databases indicate that the proposed method outperforms the state-of-the-art methods on realistic outdoor and indoor public datasets.
- KSP 제안 키워드
- Active computer vision, Background subtraction(BS), Computer Vision(CV), Counting method, Feature extractioN, Frame rate, Image resolution, Information Loss, Keypoint detector, Pedestrian Counting, Public Datasets