Event classification for vehicle navigation system by regional optical flow analysis

We address the problem of event classification for intelligent vehicle navigation system from video sequences acquired by a front mounted camera in complex urban scenes. Although in normal driving condition, large variety of events could be found and be preferably attached to an alerting system in a vehicle, there have been relatively narrow research activities on driving scene analysis, for example, finding local information such as lanes, pedestrians, traffic signs or light detections. Yet, the above-mentioned methods only provide limited performance due to many challenges in normal urban driving conditions, i.e. complex background, inhomogeneous illumination, occlusion, etc. In this paper, we tackle the problem of classification of various events by learning regional optical flows to detect some important events (very frequent occurring and involving riskiness on driving) using low cost front mounted camera equipment. We approached the problem as follows: First, we present an optical flow-based event detection method by regional significance analysis with the introduction of a novel significance map based on regional histograms of flow vectors; Second, we present a global and a local method to robustly detect ego-motion-based events and target-motion-based events. In our experiments, we achieved classification accuracy about 91% on average tested with two classifiers (Bayesian and SVM). We also show the performance of the method in terms of computational complexity achieving about 14.3 fps on a laptop computer with Intel Pentium 1.2 Ghz.