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During recent years correlation tracking is considered fast and effective by the virtue of circulant structure of the sampling data for learning phase of filter and Fourier domain calculation of correlation. During the occurrence of occlusion, motion blur and out of view movement of target, most of the correlation filter based trackers start to learn using erroneous samples and tracker starts drifting. Currently, adaptive correlation filter based tracking algorithms are being combined with redetection modules. This hybridization helps in redetection of the target in long term tracking. The redetection modules are mostly classifier, which classify the true object after tracking failure occurrence. The methods perform favorable during short term occlusion or partial occlusion. To further increase the tracking efficiency specifically during long term occlusion, while maintaining real time processing speed, this study proposes tracking failure avoidance method. We first propose, a strategy to detect the occlusion using two cues from the response map i.e., peak correlation score and peak to side lobe ratio. After successful detection of tracking failure, second strategy is proposed to save the target being getting more erroneous. Kalman filter based predictor continuously predicts the location during occlusion. Kalman filter passes this result to Support Vector Machine (SVM). When the target reappears in frame, support vector machine based classifier classifies the correct object using the predicted location of Kalman filter. This decreases the chance of tracking failure as Kalman filter continuously updates itself during occlusion and predicts the next location using its own previous prediction. Once the true object is detected by classifier after the clearance of occlusion, this result is forwarded to correlation filter tracker to resume its operation of tracking and updating its parameters. Together these two proposed schemes show significant improvement in tracking efficiency. Furthermore, this collaboration in redetection phase shows significant improvement in the tracking accuracy over videos containing six challenging aspects of visual object tracking as mentioned in the literature.

AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration