Moving Object Detection and Tracking using SIFT with K-Means Clustering

SKU: PAN_IPM_213 Categories: ,


The project presents moving object detection based on the SIFT algorithm for video surveillance system. The object detection will be approached to clustering objects from the foreground with the absence of background noise.

Initially, it starts with feature matching by choosing the start frame or taking initial few frames with the approximate median method.

Then, the complex wavelet transform is applied to both current and initialized background frame generates sub-bands of low and high frequencies.

Frame differencing will be done in this sub-bands followed by edge map creation and image reconstruction. After the object detection, the performance of the method will be measured (between frame ground truth and obtained result) through metrics such as sensitivity, accuracy, correlation and peak signal to noise ratio.

This object detection also helps to track detected object using connected component analysis. The simulated result shows that used methodologies for effective object detection have better accuracy and with less processing time consumption rather than existing methods.?

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