Recent advances and trends in visual tracking: A review
Neurocomputing
iSpy: automatic reconstruction of typed input from compromising reflections
Proceedings of the 18th ACM conference on Computer and communications security
Real-Time methods for long-term tissue feature tracking in endoscopic scenes
IPCAI'12 Proceedings of the Third international conference on Information Processing in Computer-Assisted Interventions
Ultra-fast tracking based on zero-shift points
Image and Vision Computing
Local features classification for adaptive tracking
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A multiple face detection and tracking system based on TLD
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Analyzing growing plants from 4D point cloud data
ACM Transactions on Graphics (TOG)
Emotion recognition with boosted tree classifiers
Proceedings of the 15th ACM on International conference on multimodal interaction
| Hi-index | 0.00 |
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories in video sequences. We demonstrate that the approach is complementary to commonly used normalized cross-correlation (NCC). Based on the error, we propose a novel object tracker called Median Flow. State-of-the-art performance is achieved on challenging benchmark video sequences which include non-rigid objects.