Abstract
Computer vision has been proven a remarkable entity in modern computer science. Different applications of this field have been used on a regular basis. In this paper, we propose a new model for tracking real time object (i.e., car, human face, interior objects, arms, etc.) from video feed by providing training with HAAR features and also with the implementation of Lucas Kanade Optical Flow including Custom Masking technique. Object tracking has been considered to be very much useful in augmented reality, security, virtual reality, training with simulation, etc. In this research, we have trained our classifier of a specific object for detection purpose. Upon successful training of the classifier, a video footage has been passed into the classifier. Firstly, it detects region of interest (ROI) consisting of the object(s). Secondly, with necessary preprocessing techniques we have detected the contour area enclosing the object. Finding out the contour within the detected object(s) enabled us to create green color within the contour enclosing area. We only kept green contours and subtracted everything from the frames of the scene by Custom Masking technique. Finally, we tracked the object by trailing the green contours of the detected object(s) by Lucas Kanade Optical Flow. Our developed system is able to detect and track different object(s) from a video feed with the settings of less than or equal to 30 frames per second (FPS).
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Mohiuddin, K., Alam, M.M., Das, A.K., Munna, M.T.A., Allayear, S.M., Ali, M.H. (2019). Haar Cascade Classifier and Lucas–Kanade Optical Flow Based Realtime Object Tracker with Custom Masking Technique. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_27
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DOI: https://doi.org/10.1007/978-3-030-03405-4_27
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