Abstract
Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e., affine scale invariant feature transform (ASIFT). ASIFT is used to accommodate object shift and scale object variations. ASIFT extracts certain features from the object of interest, providing invariance in up to six affine parameters, namely translation (two parameters), zoom, rotation and two camera axis orientations. However, in this paper, only the shift and scale invariances are used. The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient (APG) technique to periodically update the coordinates of the object encapsulated in the bounding box. At the end, a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented, which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.