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"Object Trackers: Approaches and Applications," a Presentation from Intel

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Minje Park, Deep Learning R&D Engineer at Intel, presents the "Object Trackers: Approaches and Applications" tutorial at the May 2019 Embedded Vision Summit.

Object tracking is a powerful algorithm component and one of the fundamental building blocks for many real-world computer vision applications. Object trackers provide two main benefits when incorporated into a localization module. First, trackers can reduce overall computation and power requirements by allowing a reduction in the frequency at which detections must be generated. Second, trackers can maintain the identity of an object across multiple frames, which is important for many applications.

Recent advances in deep learning provide us with a unified method for designing detectors, but we still have many design choices for trackers. In this talk, Park describes three basic tracker approaches and their use in video analytics applications including face recognition, people counting and action recognition. He also provides insights on how recent advances in recurrent neural networks and reinforcement learning can be used for enhancing trackers.

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[14:46] are the seems created in detection when you apply Spatial Tracking?