Object Recognition and Tracking are one of the key research areas in image processing and computer vision. This paper presents a novel technique which efficiently recognizes an object based on full boundary detection using affine scale invariant feature transform method (ASIFT). ASIFT is an improvement to SIFT algorithm as it provides invariance up to six parameters longitude and latitude wise. The six parameters are based on translation (2 parameters), rotation, camera axis orientation (2 parameters) and zoom. Key points commonly referred to as feature points are then obtained using the mentioned parameters which will recognize the object efficiently. Furthermore a region merging technique is used for object recognition and detection in the remote scene environment using ASIFT technique. A short pictorial comparison between SIFT and ASIFT will also be presented based on feature points calculation. After the recognition using ASIFT is performed, an algorithm will be presented for tracking of the recognized object using modified particle filter. The particle filter will use a proximal gradient (PG) approach for tracking of the recognized object in subsequent images. In case an object drastically varies its position w.r.t any of the six parameters mentioned above, ASIFT will again be called for object recognition.
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.
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