Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
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 submissionreaders: 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
Enter your feedback below and we'll get back to you as soon as possible.