Abstract
Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.
Highlights
Pedestrian detection is an important instant of object detection
We propose a new pedestrian detection method which combines multiple weak classifiers built on local templates by means of Random Forest
To evaluate the performance of different methods evaluated on TUD pedestrian test set, the Receiver Operating Characteristic (ROC) curves are drawn to describe the statistical comparison of different methods
Summary
Pedestrian detection is an important instant of object detection. Because of its direct applications in surveillance, intelligent traffic systems, and assisted living [1, 2], it has attracted lots of attention. A number of methods have been proposed to get robust and applied detection They can be roughly classified into 3 categories, that is, works built on holistic model [3,4,5,6,7], part/patch-based approaches [8,9,10,11,12,13,14,15,16], and detectors using multiple feature channels and boosted classifier [17,18,19,20,21,22]. They are the main parts of a human, such as head, left/right hand, and left/right leg Motivated by this observation, we propose a new pedestrian detection method which combines multiple weak classifiers built on local templates by means of Random Forest.
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