1. |
- Kristan, Matej, et al.
(författare)
-
The Visual Object Tracking VOT2015 challenge results
- 2015
-
Ingår i: Proceedings 2015 IEEE International Conference on Computer Vision Workshops ICCVW 2015. - : IEEE. - 9780769557205 ; , s. 564-586
-
Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1).
|
|
2. |
|
|
3. |
- Felsberg, Michael, 1974-, et al.
(författare)
-
The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
- 2016
-
Ingår i: Computer Vision – ECCV 2016 Workshops. ECCV 2016.. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 824-849
-
Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
|
|
4. |
- Guna, Stefan T., et al.
(författare)
-
DICE : Monitoring global invariants with wireless sensor networks
- 2014
-
Ingår i: ACM transactions on sensor networks. - : Association for Computing Machinery. - 1550-4867 .- 1550-4859. ; 10:4
-
Tidskriftsartikel (refereegranskat)abstract
- Wireless sensor networks (WSNs) enable decentralized architectures to monitor the behavior of physical processes and to detect deviations from a specified "safe" behavior, for example, to check the operation of control loops. Such correct behavior is typically expressed by global invariants over the state of different sensors or actuators. Nevertheless, to leverage the computing capabilities of WSN nodes, the application intelligence needs to reside inside the network. The task of ensuring that the monitored processes behave safely thus becomes inherently distributed, and hence more complex. In this article we present DICE, a system enabling WSN-based distributed monitoring of global invariants. A DICE invariant is expressed by predicates defined over the state of multiple WSN nodes, such as the expected state of actuators based on given sensed environmental conditions. Our modular design allows two alternative protocols for detecting invariant violations: both perform in-network aggregation but with different degrees of decentralization, therefore supporting scenarios with different network and data dynamics. We characterize and compare the two protocols using large-scale simulations and a real-world testbed. Our results indicate that invariant violations are detected in a timely and energy-efficient manner. For instance, in a 225-node 15-hop network, invariant violations are detected in less than a second and with only a few packets sent by each node.
|
|
5. |
- Kristan, Matej, et al.
(författare)
-
The Visual Object Tracking VOT2016 Challenge Results
- 2016
-
Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 777-823
-
Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.
|
|
6. |
|
|
7. |
|
|