Ego-Motion Classification for Driving Vehicle

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.