Modified YOLO Module for Efficient Object Tracking in a Video

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In the proposed work, initially, the YOLO algorithm is used to extract and classify objects in a frame. In the sequence of frames, due to various reasons the confidence measure suddenly drops. This changes the class of an object in consecutive frames which affects the object tracking and counting process severely. To overcome this limitation of the YOLO algorithm, it is modified to enable and track the same object efficiently in the sequence of frames. This will in turn increase object tracking and counting accuracy. In the proposed work drastic change in confidence scores and class change of an object in consecutive frames are identified by tracking the confidence of a particular object in the sequence of frames. These outliers are detected and removed using the RANSAC algorithm. After the removal of the outliers, interpolation is applied to get the new confidence score at that point. By applying the proposed method a smooth confidence measure variation is obtained across the frames. Using this, average counting accuracy has been increased from 66 % to 87 % and overall average object classification accuracy is in the range of 94 - 96 % for various standard dataset.