Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> CAMshift for People Detection <s> In this paper, we present a new object tracking approach in grey-level image sequences. The method is based on the analysis of the histogram of distribution of the image and the feature points of the object. Among the essential contributions of this work, we can quote a better modelling of the object to track and the consideration of the target appearance changes during the sequence. Our approach is a prolongation of the CamShift algorithm (Continuously Adaptive MeanShift) applications. The goal is to widen the field of application of this algorithm in order to adapt it to grey-level image sequences presenting strong modifications of shape, luminosity and grey-level values. <s> BIB001 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> CAMshift for People Detection <s> In this paper we describe a Camshift implementation on mobile robotic system for tracking and pursuing a moving person with a monocular camera. Camshift algorithm uses color distribution information to track moving object. It is computationally efficient for working in real-time applications and robust to image noise. It can deal well with illumination changes, shadows and irregular objects motion (linear/non-linear). We compared the Camshift with a HSV color based tracking and our results show that the Camshift method outperformed the HSV color based tracking. Moreover, the former method is much more robust against different illumination conditions. <s> BIB002 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> CAMshift for People Detection <s> To accomplish real-time tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a set of real-time tracking system. The tracking system uses a combination of camshift and kalman filter algorithm. When the moving object is a large area blocked, the velocity of moving object is applied linear prediction to kalman filter tracking. When the camera moves, we can quickly find the target in this tracking system. The experimental results show that : this system designed for tracking moving targets has good robustness. <s> BIB003 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> CAMshift for People 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 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> CAMshift for People Detection <s> In order to improve the tracking performance, an improved CamShift tracking algorithm is proposed by strengthening the distinguishing hue characteristics. The distinguishing hue of the target is determined by contrasting the target and its surrounding area. The target model is described by a weighted hue histogram. Max weights are given to the distinguishing hues to distinguish the target from the background. The probability projection image is established by the weighted histogram model. Simulation results show that the improved method has better tracking performance than the common method, and the distinguishing hue characteristics can restrain the disturbance of the background. <s> BIB005
The CAMshift algorithm has been used as detection method for people tracking BIB002 , BIB003 , BIB001 , BIB005 and  . The algorithm applies color data to probability distribution, especially hue data in HSV. The color probability distribution map called back projected image is made and used for tracking operations BIB004 . As this algorithm finds the color probability distribution to track the object, applying directly on the given video did not give the expected result. This is due to the fact that the color distribution may be dominated by the static background. To minimize the effect of the static background, a background subtraction can be used before applying the CAMshift. The result of CAMshift detection is given in Fig 2. Out of the six people tracked in the ground truth, only one person is detected. This is because of the fact that when applying background subtraction, people in very slow motion will be treated as static object and removed. In this specific lab threshold is used to remove the noise coming from the people moving slowly. From the actual video, the person going out of the shop is moving fast comparing with the other people in the video. The question is why the centroids of the CAMshift detection are lower than the centroids of the ground truth. This may be due to the high variation in the half lower part of the person's body including his hands and feet. 