Pedestrian Detection in Infrared Images Using Fast RCNN

Abstract

Compared to visible spectrum image the infrared image is much clearer in poor lighting conditions. Infrared imaging devices are capable to operate even without the availability of visible light, acquires clear images of objects which are helpful in efficient classification and detection. For image object classification and detection, CNN which belongs to the class of feed-forward ANN, has been successfully used. Fast RCNN combines advantages of modern CNN detectors i.e. RCNN and SPPnet to classify object proposals more efficiently, resulting in better and faster detection. To further improve the detection rate and speed of Fast RCNN, two modifications are proposed in this paper. One for accuracy in which an extra convolutional layer is added to the network and named it as Fast RCNN type 2, the other for speed in which the input channel is reduced from three channel input to one and named as Fast RCNN type 3.Fast RCNN type 1 has better detection rate than RCNN and compare to Fast RCNN, Fast RCNN type 2 has better detection rate while Fast RCNN type 3 is faster.