Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking

Minyoung Kim, Stefano Alletto, Luca Rigazio

Oct 15, 2016 (modified: Oct 16, 2016) NIPS 2016 workshop MLITS submission readers: everyone
  • Abstract: Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high performance tracking is still challenging, with state-of-the-art systems resulting in high complexity with a large number of hyper parameters. In this paper, we focus on reducing overall system complexity and the number hyper parameters that need to be tuned to a specific environment. We introduce a novel tracking system based on similarity mapping by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance and geometric information, and is trainable end-to-end. Our sy
  • Conflicts: us.panasonic.com, unimore.it

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