1 |
李磊磊. 自动驾驶汽车产业发展研究及展望 [J]. 汽车文摘, 2023(9): 1-10.
|
|
LI L L. Development research and prospect on automated driving vehicle industry[J]. Automative Digest, 2023(9): 1-10.
|
2 |
PARK Y, DANG L M, LEE S, et al. Multiple object tracking in deep learning approaches: a survey [J]. Electronics, 2021, 10(19): 2406.
|
3 |
ZHOU X, WANG D, KRÄHENBÜHL P. Objects as points [J]. arXiv preprint arXiv:, 2019.
|
4 |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection [J]. arXiv preprint arXiv:, 2020.
|
5 |
FARHADI A, REDMON J. Yolov3: an incremental improvement[C]. Proceedings of the Computer Vision and Pattern Recognition, F, 2018. Springer Berlin/Heidelberg, Germany.
|
6 |
SUN P, ZHANG R, JIANG Y, et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, F, 2021.
|
7 |
BEWLEY A, GE Z, OTT L, et al. Simple online and realtime tracking[C]. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), F, 2016. IEEE.
|
8 |
ZHANG Y, SUN P, JIANG Y, et al. Bytetrack: multi-object tracking by associating every detection box[C]. Proceedings of the European Conference on Computer Vision, F, 2022. Springer.
|
9 |
ZHOU X, KOLTUN V, KRäHENBüHL P. Tracking objects as points[C]. Proceedings of the European Conference on Computer Vision, F, 2020. Springer.
|
10 |
BERGMANN P, MEINHARDT T, LEAL-TAIXE L. Tracking without bells and whistles[C]. Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, F, 2019.
|
11 |
MEINHARDT T, KIRILLOV A, LEAL-TAIXE L, et al. Trackformer: multi-object tracking with transformers[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, F, 2022.
|
12 |
SUN P, CAO J, JIANG Y, et al. Transtrack: multiple object tracking with transformer [J]. arXiv preprint arXiv:, 2020.
|
13 |
ZENG F, DONG B, ZHANG Y, et al. MOTR: end-to-end multiple-object tracking with transformer[C]. Proceedings of the Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXVII, F, 2022. Springer.
|
14 |
WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[C]. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), F, 2017. IEEE.
|
15 |
PANG J, QIU L, LI X, et al. Quasi-dense similarity learning for multiple object tracking[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, F, 2021.
|
16 |
ZHANG Y, WANG C, WANG X, et al. Fairmot: on the fairness of detection and re-identification in multiple object tracking [J]. International Journal of Computer Vision, 2021, 129: 3069-3087.
|
17 |
LIANG C, ZHANG Z, ZHOU X, et al. Rethinking the competition between detection and reid in multiobject tracking [J]. IEEE Transactions on Image Processing, 2022, 31: 3182-3196.
|
18 |
MILAN A, LEAL-TAIXé L, REID I, et al. MOT16: a benchmark for multi-object tracking [J]. arXiv preprint arXiv:, 2016.
|
19 |
AHARON N, ORFAIG R, BOBROVSKY B Z. BoT-SORT: robust associations multi-pedestrian tracking [J]. arXiv preprint arXiv:, 2022.
|
20 |
CAO J, WENG X, KHIRODKAR R, et al. Observation-Centric SORT: rethinking SORT for robust multi-object tracking [J]. arXiv preprint arXiv:, 2022.
|
21 |
XU J, WANG X. Rethinking self-supervised correspondence learning: a video frame-level similarity perspective[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, F, 2021.
|
22 |
WANG Z, ZHENG L, LIU Y, et al. Towards real-time multi-object tracking[C]. Proceedings of the European Conference on Computer Vision, F, 2020. Springer.
|
23 |
BROWN R G, HWANG P Y. Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions [J]. Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions, 1997.
|
24 |
LEAL-TAIXé L, PONS-MOLL G, ROSENHAHN B. Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker[C]. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV workshops), F, 2011. IEEE.
|
25 |
YANG F, ODASHIMA S, MASUI S, et al. Hard to track objects with irregular motions and similar appearances? make it easier by buffering the matching space[C]. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, F, 2023.
|
26 |
WANG Z, ZHAO H, LI Y L, et al. Do different tracking tasks require different appearance models? [J]. Advances in Neural Information Processing Systems, 2021, 34: 726-738.
|
27 |
YU F, XIAN W, CHEN Y, et al. BDD100K: a diverse driving video database with scalable annotation tooling [J]. arXiv preprint arXiv:, 2018.
|
28 |
BERNARDIN K, STIEFELHAGEN R. Evaluating multiple object tracking performance: the clear mot metrics [J]. EURASIP Journal on Image Video Processing, 2008, 2008: 1-10.
|
29 |
RISTANI E, SOLERA F, ZOU R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]. Proceedings of the European Conference on Computer Vision, F, 2016. Springer.
|
30 |
CHEN L, AI H, ZHUANG Z, et al. Real-time multiple people tracking with deeply learned candidate selection and person re-identification[C]. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), F, 2018. IEEE.
|