Abstract
Object tracking for collision avoidance systems benefits from current progress in object detection by deep learning. For the purpose of collision avoidance, a hazard has to be tracked in several frames before the safety system can determine its future trajectory and issue a necessary warning for braking.
Because the detected object is defined by a rectangular boundary, it can represent (a non-rectangular) object as well as its background, thus leading to misleading tracking information. Therefore, we rely on feature points identified in the detected regions over time for performing feature point tracking. Feature points in the background are removed by performing clustering in real-world co-ordinates using iterative semi-global matching stereo as well as an approximate size of the detected object type.
While matching the feature points between consecutive frames, a best match might not be found. In such circumstances, initially an optimally tracked feature point is used for updating the tracking information of the mismatched feature point. However, with too many mismatches (possibly due to occlusion) its information is overwritten by a more recently matched feature point. We evaluated our system on created test video data involving a controlled collision course.
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References
Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: Proceedings of the IEEE International Conference on Computer Vision Pattern Recognition (2012)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56
Caffe implementation of Google MobileNet SSD detection network. github.com/chuanqi305/MobileNet-SSD
Howard, A., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. J. CoRR, vol. 1704.04861 (2017)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)
Khan, W., Morris, J.: Safety of stereo driver assistance systems. In: Proceedings of the IEEE Symposium Intelligent Vehicles, pp. 469–475 (2012)
Khan, W., Klette, R.: Stereo accuracy for collision avoidance for varying collision trajectories. In: Proceedings of the IEEE Symposium Intelligent Vehicles (2013)
Khan, W., Klette, R.: Accuracy of trajectories estimation in a driver-assistance context. In: Huang, F., Sugimoto, A. (eds.) PSIVT 2013. LNCS, vol. 8334, pp. 47–58. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-53926-8_5
Khan, W., Klette, R.: Stereo-matching in the context of vision-augmented vehicles. In: Proceedings of the International Symposium Visual Computing (2015)
Klette, R.: Concise Computer Vision. UTCS. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6320-6
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of the IEEE International Conference on Computer Vision (2011)
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. J. CoRR, vol. 1506.02640 (2015)
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. J. CoRR, vol. 1506.01497 (2015)
Rosebank, A.: Object detection with deep learning and OpenCV. www.pyimagesearch.com/2017/09/11/ (2017)
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Khan, W., Klette, R. (2018). Accuracy of a Driver-Assistance System in a Collision Scenario. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_23
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DOI: https://doi.org/10.1007/978-3-030-03801-4_23
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