Abstract
Detection and tracking of a human in a video is useful in many applications such as video surveillance, content retrieval, patent monitoring etc. This is the first step in many complex computer vision algorithms like human activity recognition, behavior understanding and emotion recognition. Changing illumination and background are the main challenges in object/human detection and tracking. We have proposed and compared the performance of two algorithms in this paper. One continuously update the background to make it adaptive to illumination changes and other use depth information with RGB. It is observed that use of depth information makes the algorithm faster and robust against varying illumination and changing background. This can help researchers working in the computer vision to select the proper method of object detection.
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Sardeshmukh, M.M., Kolte, M., Joshi, V. (2016). Performance Analysis of Human Detection and Tracking System in Changing Illumination. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_8
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DOI: https://doi.org/10.1007/978-3-319-47952-1_8
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