ASIFT based recognition of fixed shape moving objects and tracking via modified particle filters

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.

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