JacobChen1998/Self-adaptive-Feature-tracking-with-PCA-Lucas-Kanade

The inability to change size has always been a drawback of sliding window tracking. If the previous frame of the current frame is used as the reference frame, the error rate is often superimposed. If only traditional feature tracking methods such as SIFT, SURF or Lucas-Kanade are used, it is not possible to track a specific object and there is no defined object frame to define the overall features of the object to be tracked. Using Deep Learning (DL) for object tracking such as Siamese Tracker requires training of the object to be tracked, and the size of the tracking bounding box cannot be defined arbitrarily while tracking. We propose to use Principal Component Analysis (PCA) as the feature extraction mechanism and Lucas-Kanade (LK) tracking optical flow as the object size prediction: 1. no time-consuming DL training is required for the objects. 2. 2. The object frame size can be defined arbitrarily. 3. 3. Automatically detects and adjusts object size even if it changes.

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