In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate. In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density.The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of the local densities in the Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented.
A significant task in video intelligence systems is the extraction of information about a moving objects e.g. detecting a moving crowd with PedCount (a pedestrian counter system using CCTV) was developed by Tsuchikawa @cite_23 . It extracts the object using the one line path in the image by background subtraction to make a space-time (X-T) binary image. The direction of each travelling pedestrian is realized by the attitude of pedestrian region in the X-T image. They reported the need of background image reconstruction due to image illumination change. An algorithm to distinguish moving object from illumination change is explained based on the variance of the pixel value and frame difference.
@cite_23
2137519983 2484482231 2101611399 2133986780
The paper presents a moving object extraction method robust against illumination level changes for a real-time pedestrian counting system based on an x-t space-time image. This system, called the Ped-Counter, counts the number of pedestrians using TV camera images and image processing. The TV camera takes images of a path or the entrance of a store or an exhibition hall. A practical location for the counting system is under constant illumination conditions, such as under an arcade or in a room. If the system is used in other locations and under different conditions, for example, an outdoor path or an entrance where there is direct sunlight, then it must be robust against illumination level changes. The paper proposes two new processes for extracting moving objects from space-time images using background subtraction. The first is a background image reconstruction process using statistical characteristic analysis of temporal changes of a target pixel. The other is a moving object extraction process using delayed subtraction. These processes make the system robust against illumination level changes. This method is evaluated over a half-year period at an entrance experiencing drastic illumination level changes. We also confirm that this method can extract moving objects, that it is robust against season, weather, and time, and that the Ped-Counter is accurate. We propose a computer vision-based pipeline that enables altering the appearance of faces in videos. Assuming a surveillance scenario, we combine GMM-based background subtraction with an improved version of the GrabCut algorithm to find and segment pedestrians. Independently, we detect faces using a standard face detector. We apply the neural art algorithm, utilizing the responses of a deep neural network to obfuscate the detected faces through style mixing with reference images. The altered faces are combined with the original frames using the extracted pedestrian silhouettes as a guideline. Experimental evaluation indicates that our method has potential in producing de-identified versions of the input frames while preserving the utility of the de-identified data. This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 can be achieved. This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on motion information or detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames second, detects pedestrians at very small scales (as small as 20 × 15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: (i) development of a representation of image motion which is extremely efficient, and (ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).