Abstract
Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.
Highlights
Because the detection of moving objects is demanded in various areas, including surveillance camera system functions, it is a very important research subject in computer vision
We propose a convolutional neural network (CNN)-based shadow detection method, and our research is novel in the following four ways, compared to previous works
Our research proposes a convolutional neural network (CNN)-based shadow detection method
Summary
Because the detection of moving objects is demanded in various areas, including surveillance camera system functions, it is a very important research subject in computer vision. Surveillance camera systems use the background subtraction operation, which detects the foreground to detect a moving object. Various environmental factors such as illumination change and brightness of background cause the precise foreground detection to be a very difficult task. The shadow detection error causes another problem related to human detection, because multiple people can be detected as one human. This is because size information is a key factor in detecting and recognizing humans. The effective removal of shadow is essential to template-matching, histogram-matching, and other object detection algorithm functions
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