Real-time Vehicle Detection in Infrared and Visible Images

Real time object detection is still a challenging computer vision problem in uncontrolled environments. Unlike traditional classification problems, where the training data can properly describe the statistical models, it is much harder to discriminate certain object class from rest of the world with limited negative training samples. Due to the large variation of negatives, sometimes the intra-object class difference may be even larger than the difference between objects and non-objects. Besides this, there are many other problems that obstruct object detection, such as pose variation, illumination variation and occlusion. Previous studies also demonstrated that infrared (IR) imagery provides a promising alternative to visible imagery. Detectors using IR imagery are robust to illumination variations and able to detect object under all lighting conditions including total darkness, where detectors based on visible imagery generally fail. However, IR imagery has several drawbacks, while visible imagery is more robust to the situations where IR fails. This suggests a better detection system by fusing the information from both visible and IR imagery. Moreover, the object detector needs exhaustive search in both spatial and scale domain, which inevitably lead to high computation load. In this paper, we propose to use boosting based vehicle detection in both infrared and visible imagery. Final decision will be a combination of detection results from both the IR and visible images. Experiments are carried out using ATR helmet device with both EO and IR sensors.