In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a troublesome task especially while tracking the object over successive frames. Numerous methods have been proposed over the years for efficient detection of object of interest in a remote scene environment while in he meanwhile discarding all those which aren’t of interest and thus considered as noise. It is still one of the most actively researched areas in the field of image processing and computer vision. In this paper, a method is proposed which will not only detect a fixed shape object in a remote scene environment but it will also track it over successive frames. However, an additional methodology is also proposed which will detect the object in case of change of viewing angles e.g. scenario’s like rotation of object, zooming etc. First, Scale Invariant Feature Transform (SIFT) will be presented which will provide invariance up to four different parameters i.e. rotation, translation and zoom. In the second phase, ASIFT will be used which will provide invariance up to six different parameters i.e. translation, rotation, zoom and camera axis orientations. After both algorithms are presented, a detailed comparison between both is presented. Detection of object is performed with the help of both SIFT and ASIFT and then comparison is made based on feature points. Finally, Tracking is performed based on Proximal Gradient Particle filters which will further strengthen the comparison between SIFT and ASIFT once the object that needs to be tracked changes its course of motion or zoom. Experimental results will show which one of the two filters is more efficient.
Haris Masood, Saad Rehman, Qaiser Chaudry, Farhan Riaz, Ali Hassan, and Umar Farooq, "Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490Z (Presented at SPIE Defense + Security: April 19, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2305266.
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