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 method which allowed people counting based on video texture synthesis and to reproduce motion in a novel way was introduced by Heisele and Woehler @cite_12 . The method works under the assumption that people can be segmented from the moving background by means of appearance or motion properties. The scene image is clustered based on the color and position (R, G, B, X, Y) of pixel. The appearance of each pixel in a video frame is modelled as a mixture of Gaussian distributions. A algorithm is used that matches a spherical crust template to the foreground regions of the depth map. Matching is done by a time delay neural network for object recognition and motion analysis.
@cite_12
2167331599 2164720308 1997702526 2475101367
Our goal is to segment a video sequence into moving objects and the world scene. In recent work, spectral embedding of point trajectories based on 2D motion cues accumulated from their lifespans, has shown to outperform factorization and per frame segmentation methods for video segmentation. The scale and kinematic nature of the moving objects and the background scene determine how close or far apart trajectories are placed in the spectral embedding. Such density variations may confuse clustering algorithms, causing over-fragmentation of object interiors. Therefore, instead of clustering in the spectral embedding, we propose detecting discontinuities of embedding density between spatially neighboring trajectories. Detected discontinuities are strong indicators of object boundaries and thus valuable for video segmentation. We propose a novel embedding discretization process that recovers from over-fragmentations by merging clusters according to discontinuity evidence along inter-cluster boundaries. For segmenting articulated objects, we combine motion grouping cues with a center-surround saliency operation, resulting in “context-aware”, spatially coherent, saliency maps. Figure-ground segmentation obtained from saliency thresholding, provides object connectedness constraints that alter motion based trajectory affinities, by keeping articulated parts together and separating disconnected in time objects. Finally, we introduce Gabriel graphs as effective per frame superpixel maps for converting trajectory clustering to dense image segmentation. Gabriel edges bridge large contour gaps via geometric reasoning without over-segmenting coherent image regions. We present experimental results of our method that outperform the state-of-the-art in challenging motion segmentation datasets. A spatiotemporal saliency algorithm based on a center-surround framework is proposed. The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery. Under this formulation, the saliency of a location is equated to the power of a predefined set of features to discriminate between the visual stimuli in a center and a surround window, centered at that location. The features are spatiotemporal video patches and are modeled as dynamic textures, to achieve a principled joint characterization of the spatial and temporal components of saliency. The combination of discriminant center-surround saliency with the modeling power of dynamic textures yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras. The related problem of background subtraction is treated as the complement of saliency detection, by classifying nonsalient (with respect to appearance and motion dynamics) points in the visual field as background. The algorithm is tested for background subtraction on challenging sequences, and shown to substantially outperform various state-of-the-art techniques. Quantitatively, its average error rate is almost half that of the closest competitor. We conducted high-frame-rate (HFR) video mosaicing for real-time synthesis of a panoramic image by implementing an improved feature-based video mosaicing algorithm on a field-programmable gate array (FPGA)-based high-speed vision platform. In the implementation of the mosaicing algorithm, feature point extraction was accelerated by implementing a parallel processing circuit module for Harris corner detection in the FPGA on the high-speed vision platform. Feature point correspondence matching can be executed for hundreds of selected feature points in the current frame by searching those in the previous frame in their neighbor ranges, assuming that frame-to-frame image displacement becomes considerably smaller in HFR vision. The system we developed can mosaic 512×512 images at 500 fps as a single synthesized image in real time by stitching the images based on their estimated frame-to-frame changes in displacement and orientation. The results of an experiment conducted, in which an outdoor scene was captured using a hand-held camera-head that was quickly moved by hand, verify the performance of our system. This paper presents a method for synthesizing a stroboscopic image of a moving sports player from a hand-held camera sequence. This method has three steps: synthesis of background image, synthesis of stroboscopic image, and removal of player’s shadow. In synthesis of background image step, all input frames masked a bounding box of the player are stitched together to generate a background image. The player is extracted by an HOG-based people detector. In synthesis of stroboscopic image step, the background image, the input frame, and a mask of the player synthesize a stroboscopic image. In removal of shadow step, we remove the player’s shadow which negatively affects an analysis by using mean-shift. In our previous work, synthesis of background image has been timeconsuming. In this paper, by using the bounding box of the player detected by HOG and by subtracting the images for synthesizing a mask, computational speed and accuracy can be improved. These have contributed greatly to the improvement from the previous method. These are main improvements and novelty points from our previous method. In experiments, we confirmed the effectiveness of the proposed method, measured the player’s speed and stride length, and made a footprint image. The image sequence was captured under a simple condition that no other people were in the background and the person controlling the video camera was standing still, such like a motion parallax was not occurred. In addition, we applied the synthesis method to various scenes to confirm its versatility.