Abstract
Multi-target tracking is a challenging task and becomes more so when both camera and targets are in motion and the targets have similar appearances with frequent occlusions. To maintain a proper track in such scenarios, individual target representation and accurate data association methods are prime requirements for a robust multi-target tracker. We observe that a target can be modeled as a subspace by using its feature vectors over several consecutive frames. We propose an adaptive subspace model to handle the large range of target variations throughout the track. We also develop a novel two-step parallel scheme for data association which exploits scale and location information along with appearance information to distinguish the targets. The track results for challenging videos (containing occlusions and variations in pose and illumination) indicate that the proposed method achieves better/comparable tracking accuracy in comparison to several recent trackers.
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Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. (2008)
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2017)
Chen, J., Sheng, H., Zhang, Y., Xiong, Z.: Enhancing detection model for multiple hypothesis tracking. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)
Chen, L., Ai, H., Shang, C., Zhuang, Z., Bai, B.: Online multi-object tracking with convolutional neural networks. In: 2017 IEEE International Conference on Image Processing (ICIP) (2017) (2017)
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition 2009. CVPR 2009. IEEE (2009)
Henschel, R., Leal-Taixé, L., Cremers, D., Rosenhahn, B.: Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking. CoRR (2017)
Keuper, M., Tang, S., Yu, Z., Andres, B., Brox, T., Schiele, B.: A multi-cut formulation for joint segmentation and tracking of multiple objects. CoRR
Leal-Taixé, L., Milan, A., Reid, I.D., Roth, S., Schindler, K.: Motchallenge 2015: Towards a benchmark for multi-target tracking. CoRR (2015)
Luo, W., Zhao, X., Kim, T.: Multiple object tracking: a review. CoRR
Milan, A., Leal-Taixé, L., Reid, I.D., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. CoRR (2016)
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR (2015)
Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. CoRR (2016)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking (2008)
Shirazi, S.A., Sanderson, C., McCool, C., Harandi, M.T.: Improved object tracking via bags of affine subspaces. CoRR (2014)
Vidal, R., Ma, Y., Sastry, S.: Generalized principal component analysis (GPCA). IEEE Trans. Pattern Anal. Mach. Intell
Yan, J., Pollefeys, M.: A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 94–106. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_8
Yoon, J.H., Lee, C.R., Yang, M.H., Yoon, K.J.: Online multi-object tracking via structural constraint event aggregation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhang, X., Luo, W., Li, X., Hu, W., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model. IEEE Trans. Pattern Anal. Mach. Intell
Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters (2004)
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Nigam, J., Sharma, K., Rameshan, R.M. (2018). Detection-Based Online Multi-target Tracking via Adaptive Subspace Learning. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_24
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DOI: https://doi.org/10.1007/978-3-030-04375-9_24
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