Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> DETECTION <s> This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection. <s> BIB001 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> DETECTION <s> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. <s> BIB002 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> DETECTION <s> Human detection is the first step for a number of applications such as smart video surveillance, driving assistance systems and intelligent digital content management. Object detection is one of the most important preprocessing steps in object recognition and identification systems. It's a challenging problem due to the variance of illumination, color, scale, pose and so forth. This can be done by searching and indexing still image containing object in various size, position and background. This paper reviews various aspects of human detection in static images and focus on learning based method that builds classifiers. <s> BIB003 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> DETECTION <s> Hand gesture interfaces are more intuitive and convenient than traditional interfaces. They are the most important parts in the relationship between users and devices. Hand tracking for hand gesture interfaces is an active area of research in image processing. However, previous works have limits such as requiring the use of multiple camera or sensor, working only with single color background, etc. This paper proposes a real-time both hands tracking algorithm based on “CAMshift (Continuous Adaptive Mean Shift Algorithm)” using only a single camera in multi-color backgrounds. In order to track hands robustly, the proposed algorithm uses “motion mask” to combine color and movement probability distributions and “probability reduction” for multi-hand tracking in non-limiting environments. Experimental results demonstrate that this algorithm can precisely track both hands of an operator in multi-color backgrounds and process the VGA size input sequences from a web camera in real time (about 25 fps). <s> BIB004
The People detection is the first step in different tracking applications BIB003 . It is the process of determining the presence of a person in video frames and determines the location and size of the person. There are different human detection algorithms like Histograms of Oriented Gradient (HOG) BIB002 and CAMshift BIB004 , Viola and Jones BIB001 . However, as HOG and CAMshift has proposed by many authors, in this paper comparison of HOG and CAMshift will be presented.