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Open AccessArticle

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-Ro 1-Gil, Jung-Gu, Seoul 100-715, Korea
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Sensors 2018, 18(4), 960; https://doi.org/10.3390/s18040960
Received: 22 January 2018 / Revised: 22 March 2018 / Accepted: 22 March 2018 / Published: 23 March 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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. View Full-Text
Keywords: intelligence surveillance camera; shadow detection; color feature; CNN intelligence surveillance camera; shadow detection; color feature; CNN
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Kim, D.S.; Arsalan, M.; Park, K.R. Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor. Sensors 2018, 18, 960.

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