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A Multi-Objective Recognition Algorithm with Time and Space Fusion
Technical Paper
2019-01-1047
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Multi-target recognition technology plays an important role in the field of intelligent driving. In this paper, we propose a novel multi-target recognition algorithm with high accuracy and efficiency. We design a time series based recurrent neural network that integrates historical appearance information on the timeline, which can effectively improve the recognition accuracy. The target appearance characteristics extracted from the feature fusion network are then sent to the recursive neural network with the function of long-term and short-term memory for prediction, extending the learning and analysis of the neural network to the space-time domain. After the LSTM interprets advanced visual features, time series based regression is used as an appearance model to regress features to a particular visual element position through preliminary position inference. We evaluate our proposed algorithm on the KITTI data set and a large number of real scene experiments. Compared to other multi-target recognition methods, our algorithm provides much better accuracy. The experimental results show that the accuracy of road target algorithm can be effectively improved by learning historical visual semantics and target position information.
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Citation
Wang, H., Chi, J., Wu, C., Yu, X. et al., "A Multi-Objective Recognition Algorithm with Time and Space Fusion," SAE Technical Paper 2019-01-1047, 2019, https://doi.org/10.4271/2019-01-1047.Data Sets - Support Documents
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References
- Li , L. , Wen , D. , Zheng , N.N. et al. Cognitive Cars: A New Frontier for ADAS Research IEEE Transactions on Intelligent Transportation Systems 13 1 395 407 2012
- Zhao , W.-L. , Ngo , C.-W. et al. Flip-Invariant SIFT for Copy and Object Detection IEEE Transactions on Image Processing 22 3 980 991 2016
- Lakhani , H. and Neji , M. Hand Gesture Recognition Method Based on HOG-LBP Features for Mobile Devices Procedia Computer Science 126 2018
- Dalal , N. and Trig’s , B. Histograms of Oriented Gradients for Human Detection Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on IEEE 2005 1 886 893
- Hijazi , S. , Kumar , R. , and Rowen , C. 2015
- Szegedy , C. , Toshev , A. , and Erhan , D. Deep Neural Networks for Object Detection Advances in Neural Information Processing Systems 2553 2561 2013
- Sermanet , P. , Eigen , D. , Zhang , X. et al. 2013
- Redmon , J. , Divvala , S. , Girshick , R. et al. 2016
- Liu , W. , Anguelo , D. , Erhan , D. et al. 2016
- Ning , G. , Zhang , Z. , Huang , C. et al. IEEE International Symposium on Circuits and Systems (ISCAS)-Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking 2017 1 4 2017
- Geiger , A. , Lenz , P. , and Urtasun , R. Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite CVPR 2012
- Szegedy , C. , Liu , W. , Jia , Y.Q. , Sermanet , P. et al. Going Deeper with Convolutions 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) Boston, MA, USA IEEE 2015 1 9 10.1109/CVPR.2015.7298594
- Ren , S. , He , K. , Girshick , R. et al. 2016