상세정보
학술대회
Unsupervised Moving Object Detection through Background Models for PTZ Camera
- 발행일
- 202101
- 출처
- International Conference on Pattern Recognition (ICPR) 2020, pp.3201-3208
- 협약과제
- 20HS5300, 장기 시각 메모리 네트워크 기반의 예지형 시각지능 핵심기술 개발, 문진영
- 초록
- Moving object detection in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN-based methods usually require the user's supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.
- KSP 제안 키워드
- Appearance modeling, Background Modeling, Consensus model, Convolution neural network(CNN), Detection Method, False positive, Fine model, Gaussian Model, Moving Object Detection, Object region, PTZ camera