| 1. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 2. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 3. |
- Chen, Mo, et al.
(författare)
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Energy-Efficient and Safe-Separation Operation for Successive Trains
- 2023
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - 2153-0017 .- 2153-0009. ; , s. 845-851
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Konferensbidrag (refereegranskat)abstract
- Energy-efficient and safe-separation during train operation is of great significance for urban rail transit systems, particularly on lines with high traffic density. This paper integrates the two issues and proposes a general cooperative operation method for successive trains with no limit on the number of trains. The cooperation problem is formulated as an optimal control problem and then solved as a nonlinear program. By simultaneously optimizing the speed profiles of each train, the total traction energy of the multi-train system can be minimized, ensuring safety by imposing dynamic time headway constraints among adjacent trains throughout the entire distance horizon. Moreover, a dynamic programming method is developed for comparative study, to verify the effectiveness of the proposed method.
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| 4. |
- Xiao, Zhuang, et al.
(författare)
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Eco-Driving for Metro Trains: A Computationally Efficient Approach Using Convex Programming
- 2023
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 72:8, s. 10063-10076
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Tidskriftsartikel (refereegranskat)abstract
- Eco-driving for trains has traditionally focused on minimizing mechanical energy consumption at wheels, while completely ignoring traction chain losses that are rather significant. This paper presents a computationally efficient approach to minimize the total electrical energy consumption from traction substations (TS). After a nonlinear and non-convex program is formulated in time domain, a nonlinear and non-convex program is formulated in space domain to overcome the drawbacks of the model in time domain. By convex modeling steps, the non-convex program in space domain is reformulated as a convex program that can be efficiently solved. To further reduce computational effort, a real-time iteration sequential quadratic programming (SQP) algorithm is proposed to solve the convex program in a model predictive control framework. Numerical results indicate that the proposed SQP method yields a near-optimal solution with high computational efficiency. Compared to a traditional mechanical energy consumption model, a TS-to-traction energy efficiency can be improved.
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| 5. |
- Xiao, Zhuang, et al.
(författare)
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Energy-efficient predictive control for trams incorporating disjunctive time constraints from traffic lights
- 2023
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 151
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Tidskriftsartikel (refereegranskat)abstract
- Tram operations are often blocked by traffic lights, leading to frequent decelerations and re-accelerations that increase operational energy consumption. This paper focuses on tram energy-efficient control problem incorporating time constraints from traffic lights that have multiple feasible green time windows (GTWs). We formulate the problem as a mixed-integer nonlinear program (MINLP), where binary variables are assigned to model disjunctive time constraints of the GTWs. To address computational challenge of solving the MINLP, we reformulate it as a tractable nonlinear program (NLP). Specifically, an equivalent NLP is first presented by replacing the integrality constraint with nonlinear constraints, and then the nonlinear constraints are relaxed and penalized into cost functions. To recover a solution of the MINLP, we propose a computationally efficient sequential quadratic programming algorithm in a shrinking horizon model predictive control framework, which updates the penalty parameter and quadratic programming subproblems in parallel. The solution obtained from the subproblem is feasible in each iteration, and convergence of the feasibility iterations can be enforced by the updated penalty. The performance of the proposed approach is investigated on different scenarios using real-life tram data. Results show that the method is able to generate energy-efficient driving trajectories in a dynamic environment, while crossing traffic lights in effective GTWs without unnecessary decelerations and re-accelerations.
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