A convolutional feature map-based deep network targeted towards traffic detection and classification. (15th June 2019) - Kaur, Baljit; Bhattacharya, Jhilik
Highlights: Covariance matrix based adaptive learning rate(LR). (83%). Multimodal CNN RGB-optical flow by fusing the convolution features. (84%) Different structured NoCs trained and best used for classification. (83%) Compared results of NoC trained and tested using blurred and normal sets (74.6%). 1C3fc with adaptive LR gives better performance in blur, optical and normal sets. Abstract: Vehicle detection and classification is an important task for street surveillance and scene perception for robot navigation or autonomous vehicles. This research work focuses on traffic detection for real time applications using three components. The first component includes designing convolutional feature map-based classifier based on multimodal optical flow features. The second component is to utilize an effective adaptive learning rate technique to deal with saddle points; and to propose an average covariance matrix based pre-conditioning approach. The third component is to separately train multimodal model using blur data which caters blur effect of real time data. Extensive experimental results with different learning rates, architectures are reported using benchmark datasets such as Apollo, KITTI, Cityscapes, Berkeley, Caltech, PASCAL VOC and self created. Experimental results demonstrate that in comparison to fully connected network based classifier, Network on Convolutional (NoC) feature map classifier provided approximately 10% hike in classification accuracy without data per-processing, and almost 18% improvement with pre-processed data. The blur model enhances accuracy by almost 15% on blurred data as compared to normal RGB data. Moreover, multimodal features provide 12% and 2% higher accuracy while using standard classifiers and NoC classifiers respectively.