Vehicle Activity Recognition Using DCNN

This paper presents a novel Deep Convolutional Neural Network (DCNN) method for vehicle activity classification. We extend our previous approach to be able to classify a larger number of vehicle trajectories in a single network. We also highlight the flexibility of our approach in integrating further scenarios to our classifier. Firstly, a spatiotemporal calculus method is used to encode the relative movement between vehicles as a trajectory of QTC states. We then map the encoded trajectory to a 2D matrix using the one-hot vector mapping, this preserves the important positional data and order for each QTC state. To do this we associate the QTC sequences with pixels to form a 2D image texture. Afterwards, we adapted trained CNN architecture into our vehicles activity recognition task. Two separate types of driving data sets are used to evaluate our method. We demonstrate that the proposed method out-performs existing techniques. Along with the proposed approach we created a new dataset of vehicles interactions. Although the focus of this paper is on the automated analysis of vehicle interactions, the proposed technique is general and can be applied for pairwise analysis for moving objects.