Human Detection Methods

In recent years, many human detection approaches have been proposed. In general, the overall process of human detection could be roughly separated into three main steps: foreground segmentation, feature extraction and human recognition.

2.1.1 Foreground Segmentation

In order to reduce computational cost, the foreground segmentation is required to filter out background regions and segment the region-of-interest (ROI). There are various methods for foreground segmentation. Some are based on 2-D information, such as optical flow method, background subtraction, etc. Optical flow [12-14]

reflects the image changes due to motion during a time interval, and the optical flow field is the velocity field that represents the three-dimensional motion of foreground points across a two-dimensional image. It is accurate at detecting interesting foreground region, but it has complex computation and is hard to realize in real-time.

Background subtraction [15-18] is the most common method for segmentation of foreground regions in sequences of images. This method has to build the initial background model in order to subtract background image from current image for obtaining foreground regions. Through this method, the detected foreground regions are very complete and the computational cost is low. But this method could not be used in the presence of camera motion, and the background model must be updated continuously because of the illumination change or changeable background.

Other methods are based on some types of additional information such as infrared images [9] or depth images [5-9, 19]. The use of depth image to implement human detection would have some distinct advantages over conventional techniques.

First, it is robust to illumination change and influence of distance. Second, it could deal with occlusion problems efficiently. Third, it is suitable for moving camera because no background modeling is required. Based on the depth information, the foreground segmentation could be implemented by finding the vertical distribution of objects in the 3-D space because a human would present vertically in general.

However, implementing stereo-vision requires more than one camera and often has distance limitation.

2.1.2 Feature Extraction

Once the foreground regions are detected, different combinations of features and classifiers can be applied to make the distinction between human and non-human. The objective of feature extraction is extracting human-related features to increase detection rate, and there are many kinds of features which could be used to recognize human beings. The first kind of features is based on gradient computation, like edge [7-9], histogram of oriented gradient (HOG)[1, 20], Haar-like features [21], etc. The gradient computation aims at identifying points with brightness changing sharply or discontinuously in a digital image. Therefore, the boundaries of objects and the shape information of human could be found and extracted based on gradient computation.

Fig-2.1 shows the examples of Haar-like features. The second kind of feature is motion-based features [8, 21]. Because a human, especially a walking human, would have periodic motion, then the human could be distinguished from other objects based on the periodicity. Other features, like texture [7], skeleton [10], SIFT [22], etc., are

often used in human detection. However, because of the high variation of human appearance, it is common to use more than one kind of features to implement human detection.

2.1.3 Human Recognition

After feature extraction, the system has to distinguish the human with other objects based on the set of features. Many approaches use the techniques of machine learning to recognize humans, including support vector machine (SVM)[1, 16], artificial neural network (ANN)[9, 23, 24], AdaBoost[2, 21], etc. The main advantages of machine learning are the tolerance of variation and its learning ability.

However, it needs many training samples to make the system to learn how to judge human and non-human. Support vector machine is a powerful tool to solve pattern recognition problems. It can determine the best discriminant support vectors for human detection. Similarly, artificial neural network has been applied successfully to pattern recognition and image analysis. ANN uses a lot of training samples to make the network to be capable to judge human and non-human. AdaBoost is used to construct a classifier based on a weighted linear combination of selected features, which yield the lowest error on the training set consisting of human and non-human.

Besides machine learning, the technique of template matching [3-6, 25, 26] is also widely used in human detection. It is easy to implement and has low computational cost, but the variation tolerance is less than machine learning. In [5, 6],

Fig-2.1 Examples of Haar-like features

the system first uses head template to find possible human candidates, because the variation of human head is much less than other parts of body. Then, use other features to further judge whether the candidates are human or not. In [4], the system combines a large amount of human poses into a “template tree,” and the similar poses would be grouped together. Therefore, it could have more variation tolerance and still has low computational cost because of its tree structure. However, the process of collecting human poses and determining the similarities between different poses is time-consuming and difficult.

The methods introduced above are directly detecting the whole human shape.

However, this kind of methods has to deal with high variation and is hard to handle the occlusion problem. Therefore, component-based concept [2, 3, 25-27] is proposed to achieve higher detection rate and resolve the occlusion problems. This kind of approaches attempt to break down the whole human shape into manageable subparts.

In other words, the whole human shape is represented as a combination of parts of body. Therefore, the system doesn’t have to directly detect the whole human shape, and it could use component-based detectors to detect different parts of body. There are some advantages of component-based detection methods. First, the variation of human appearance could be highly reduced. Second, it could deal with partially occlusion. However, it might cause more computational cost and influence the detection speed.