A robust features-based person tracker for overhead views in industrial environment

A top view camera having wide range lens installed overhead of the objects contributes greatly toward resolving the tracking problem and also maintains comprehensive visual access of the environment. Video analytics becoming more important to Internet of Things applications including automatic people monitoring and surveillance systems. We followed an approach based on machine learning features-based person tracking algorithm in industrial environment. The algorithm implements simple motion detection framework through motion blobs. The algorithm, rHOG uses the history of already imaged/blobed population with the anticipated blob position of the person observed. We have compared our results, acquired through five varying test sequences, with established algorithms used for object tracking. The results highlight that our algorithm beats others tracking algorithms by greater margins. The accuracy depicted in our results shows 99% of accuracy compared to the last known best algorithm, the mean shift algorithm, yielding 48% accuracy in result. Furthermore, unlike other blob-based tracking algorithms, our algorithm has additional property to discriminate any blob as a person or no person. Our proposed tracking algorithm has the additional advantage of detecting stationary person for a long time, handling occlusion, abrupt change in the environment, and keeps performing the tracking by compensating for the gaps in data pertaining to all the frames.