Object detection using image reconstruction with PCA
In this paper, we present an object detection system and its application to pedestrian detection in still images, without assuming any a priori knowledge about the image. The system works as follows: in a first stage a classifier examines each location in the image at different scales. Then in a second stage the system tries to eliminate false detections based on heuristics. The classifier is based on the idea that Principal Component Analysis (PCA) can compress optimally only the kind of images that were used to compute the principal components (PCs), and that any other kind of images will not be compressed well using a few components. Thus the classifier performs separately the PCA from the positive examples and from the negative examples; when it needs to classify a new pattern it projects it into both sets of PCs and compares the reconstructions, assigning the example to the class with the smallest reconstruction error. The system is able to detect frontal and rear views of pedestrians, and usually can also detect side views of pedestrians despite not being trained for this task. Comparisons with other pedestrian detection systems show that our system has better performance in positive detection and in false detection rate. Additionally, we show that the performance of the system can be further improved by combining the classifier based on PCA reconstruction with a conventional classifier using a Support Vector Machine.
object detection pedestrian detection principal component analysis support vector machines