论文浏览

【论文题目】Ego-motion Classification for Driving Vehicle

【作    者】Li Du, Wenhui Jiang, Zhicheng Zhao, Fei Su        点击下载PDF全文

【关 键 字】ego-motion classification. Convolutional Neural Networks (CNNs). Long Short Term Memory (LSTM)

【发表刊物/会议】
    2017 IEEE Third International Conference on Multimedia Big Data

【摘    要】
     Accurate prediction of vehicle ego-motion in real time is crucial for an autonomous driving system. In this paper, we formulate the problem of ego-motion classification as video event detection, and we propose an end-to-end deep model to address this problem. In this model, we utilize Convolutional Neural Networks (CNNs) to extract semantic visual feature of each video frame, and employ a Long Short Term Memory (LSTM) to model the temporal correlation of the video streams. To study the performance of ego-motion classification, we constructed a video dataset-Campus20, which captured in general driving conditions. Experimental results on Campus20 verifies the superior performance of our proposed model over well established baselines.

【发 表 年】2017

【发 表 月】7

【类    别】计算机视觉


Tel: 086-010-62283118 邮编:100876
地址:北京市海淀区西土城路10号北京邮电大学教二楼多媒体中心
北京市海淀区西土城路十号113#信箱
版权所有:北京邮电大学多媒体通信与模式识别研究室 京ICP证14002347号