An Adaptive Threshold for the Canny Edge Detection with Actor-Critic Algorithm
This addresses the challenge of robust foreground detection in varying surveillance conditions, though it is incremental as it builds on existing deep learning and background subtraction methods.
The paper tackles the problem of foreground object detection in visual surveillance by proposing a spatio-temporal fusion network (STFN) that extracts temporal and spatial information, showing improved performance in environments different from training with 11.28% and 18.33% higher FM than the latest deep learning methods on LASIESTA and SBI datasets.
Visual surveillance aims to perform robust foreground object detection regardless of the time and place. Object detection shows good results using only spatial information, but foreground object detection in visual surveillance requires proper temporal and spatial information processing. In deep learning-based foreground object detection algorithms, the detection ability is superior to classical background subtraction (BGS) algorithms in an environment similar to training. However, the performance is lower than that of the classical BGS algorithm in the environment different from training. This paper proposes a spatio-temporal fusion network (STFN) that could extract temporal and spatial information using a temporal network and a spatial network. We suggest a method using a semi-foreground map for stable training of the proposed STFN. The proposed algorithm shows excellent performance in an environment different from training, and we show it through experiments with various public datasets. Also, STFN can generate a compliant background image in a semi-supervised method, and it can operate in real-time on a desktop with GPU. The proposed method shows 11.28% and 18.33% higher FM than the latest deep learning method in the LASIESTA and SBI dataset, respectively.