HOG特征与kalman滤波结合的行人检测与跟踪方法

B. Overview of the Approach
It is well known that HOG is quite a popular method of detecting people in static image. However, it can’t be used in video due to its slow computing of features. It needs to be improved if we use it in video. In [15] the detectors combine gradient based appearance descriptors with differential optical flow based motion descriptors in linear SVM framework. The method of optical flow is quite complex, it is difficult in achieving real-time without special hardware support. In this paper, we propose a new method. We all know that the scale of detection is the whole frame in the video in general. However, in our experiments, the scale of detection is narrowed greatly exception the first frame including people, making detection time is reduced enormously and the veracity of detection and tracking is improved.
Ningbo University, Ningbo, China
Abstract—Both detection and tracking people are challenging problems, because the human body is non-rigid and there is occasion between the body block. In general, human detection is a prerequisite for human tracking, and tracking has no effect on human detection. However, a novel approach is proposed for human detection and tracking in this paper, changing this situation. First, improved HOG is used to extract human features in the image. Second, we make the relationship between human detection and tracking closer—detection is not only the prerequisite of tracking and it also benefits from tracking. Finally, the Kalman filter is introduced into detecting and tracking people. Our experiments have demonstrated that such a method reduces detection time, and improves human detection and tracking accuracy.
Changyan Li Research Institute of Computer Science and Technology
Ningbo University, Ningbo, China
Lijun Guo, Yichen Hu Research Institute of Computer Science and Technology
A. Related Work
HOG feature is quite popular in human detection, Dalal and Triggs[1] is notable because it was the first paper to report
impressive results on human detection. Their works use HOG as low-level features, which were shown to outperform features such as wavelets [2], PAC-SIFT [3] and shape contexts [4]. To improve detection speed, Zhu et al. [5] propose a rejection cascade using HOG features. Zhang et al. [6] propose a multiresolution framework to reduce the computational cost. Begard et al. [7] address the problem of real-time pedestrian detection by considering different implementations of the Adaboost algorithm.
2010 3rd International Congress on Image and Signal Processing (CISP2010)
A New Method Combining HOG and Kalman Filter
for Video-based Human Detection and Tracking
In this paper, we make the relationship between human detection and tracking closer. Our method consists of the following steps: Firstly, we detect people in the video by the improved HOG. When we detect people for the first time, we remember some useful information, such as location and so on, among which will be used in the Kalman filter initializatiotion; human tracking; Kalman filter
I. INTRODUCTION
Both detection and tracking people are important for many applications, such as automotive safety, surveillance, virtual reality, intelligent control, human-computer interaction and model-based coding and so on. For these applications, we need to detect the objects of interest first (i.e., find the image regions corresponding to the objects) and then track them across different frames while maintaining the correct identities. The two principal sources of difficulty in performing this task are: (a) changes in appearance of the objects with viewpoint, illumination and clothing, and (b) partial occlusion of objects of interest by other objects. In this paper, we present a novel approach, resolving some of these problems, for detecting and tracking human in a walking. Our system does not rely on motion information; instead, it detects humans based on improved HOG that is well used in human detection, and it has become a mainstream approach. There are three important contributions in this paper. Firstly, we apply improved HOG[5] which saves computing time. Secondly, we make the relationship between human detection and tracking closer, that is to say, detection is not only the prerequisite of tracking and it also benefits from tracking. Tracking provides some useful information for human detection making detection time less. Thirdly, The Kalman filter is used to predict and estimate human state in the next frame, which makes the accuracy of human body tracking enhanced. At the same time, it can solve some special shielding problems.