Both peopledetection and human pose estimation have a large variety of applications such as automotive safety, surveillance, and video indexing. The goal of this paper is to develop a generic model for human detection and pose es- timation that allows to detect upright people (i.e., pedestri- ans ), as well as highly articulated people (e.g., in sports scenes ), and to estimate their poses. Our model should also enable upper body detection and pose estimation , e.g., for movie indexing. The top performing methods for these three scenarios do currently not share the same archi- tecture, nor are components necessarily similar either. Here, we present a generic approach that allows for both human detection and pose estimation thereby addressing the above mentioned scenarios in a single framework. Due to its care- ful design the proposed approach outperforms recent work on three challenging datasets (see Fig. 1 for examples).
We have proposed a new method to classify moving objects and recognize people for automated visual surveillance by their gait. Multiple objects are tracked successfully through the use of shape-based parameters to allocate them to different layers. Problems encountered during tracking such as background clutter, appearance of uninteresting objects and entry and exit of objects are handled efficiently. Finally moving regions are classified into either a single walking person, group of people or an undefined object such as vehicle. We have explored an alternative technique for walking peopledetection based on their gait motion. The experimental results confirm the robustness of our method to discriminate between moving objects with a detection rate of %100. For people recognition, a new model-based method is described to extract the joints positions via an evidence gathering technique. Spatial model templates for human motion are described in a parametrized form using the Fourier descriptor. The proposed solution has achieved a classification rate of %92 for people recognition. The model-based is suited to more generalized deplyment and this will be the focus for future work.
Calculating the integral image directly from the JPEG co- eﬃcients has the obvious advantage of eliminating the need for an explicit integrator. In fact, calculating the integral im- age directly is equivalent to decompressing the image. One might wonder why linear interpolation is used instead of simply storing a smaller image, since the images are essen- tially equivalent. Although a high-resolution image is not re- quired by this algorithm to detect people, the features will be misaligned at large scales unless they are placed at what is es- sentially subpixel resolution at small scales. The method that was chosen to achieve this was to duplicate pixels to allow more precise placement of features. Although this could have been achieved by fully decompressing a smaller image, it was evaluated that the bottleneck was more likely to be in stor- ing the image to memory rather than in receiving the com- pressed data. A tradeo ﬀ can be achieved between the size of the input stream and the complexity of the on-chip decom- presser. This is due to the observation that JPEG decompres- sion does not scale linearly with the resolution. While a full- resolution decompression would require 64 accumulators, one for each pixel in the block, a (1 / 4)-resolution scan only requires 4 accumulators, or 1 / 16th of that needed for the full resolution. To give a feel for the amount of resources saved by this method, the module calculating the 4 exact points takes up 400 slices in a Virtex-II FPGA (each slice contains 2 flip-flops and 2 four-input lookup tables). The modules approximating the remaining 60 points take up collectively less than 100 slices. Even by limiting estimates to the storage space required for the DCT coeﬃcients’ accumulators, cal- culating the exact values of the 60 remaining integral-image points would require more than taking 960 slices. This would have severe impacts on both placement and routing e ﬀ orts for the entire module, possibly resulting in a reduced mini- mum period.
The human factor can be considered as one of the most significant vulnerability; but unfortunately, it is often left unaddressed . Organizations will not be able to protect the integrity, confidentiality, and availability of information assets if they ignore the human factor. In most organizations, managing information security threats focuses on managing technology and process, but little efforts are paid at managing people. A study by Ashenden  reaches that the human factor of information security management has largely been neglected. In fact, a small number of publications have actually addressed the human aspect of information security –.
Shadow detection and removal has become very important in image processing. Satellite images contain shadows of various objects like buildings, trees, clouds, etc., which will hide the information of the underlying objects. The presence of shadows in images has both advantages and disadvantages. The shadows in images help in identifying the size and shape of the building which is useful for urban planning and reconstruction of scenes. Shadows also help to evaluate the size and the shape of buildings. The disadvantage of the presence of shadows is that they hide the information of the concealed objects, result in fake color tones, and distort the object’s shape.
The research at automated surveillance systems aims to automate objects discovery and distinguish whether it is a target or not. Where it is possible that the moving object is human being or another organism like cats or dogs or etc. In the first case after distinguish it is a human being the system must be react by recognizing the character and a decision must be taken regarding it. Discovering people process usually related to a set algorithms and steps that is used to filters the received images and applying some image processing techniques like convert image to gray scale. Then in order to remove noisy and abnormal point, the predication of threshold to black and white colors converting is required. Next, some operations are applied by using filters and some techniques of image processing on related components which are called
Object detection is a task with different issues regarding how the objects appear in images. First, there was the question of how to find/detect instances using their shapes (Borgefors, 1988); object were matched to a template that is rigid to any object variations, thus more convenient ways to describe object were required. Oren et al. (1997) proposed a detection framework that has influenced many nowadays detectors; they proposed an exhaustive approach for scanning images for any instance using sliding windows where Haar wavelet features are extracted then a support vector machine (machine learning) classifies whether instances are there or not. Viola and Jones (2001) introduced their face detection (a specific domain in object detection) framework and went successful for real-time performance thanks to the fast computing in Haar wavelets and the integral image.
The output of the change detection module is the bi- nary image that contains only two labels, i.e., ‘0’ and ‘255’, representing as ‘background’ and ‘foreground’ pixels respectively, with some noise. The goal of the connected component analysis is to detect the large sized connected foreground region or object which is one of the important operations in motion detection. The pixels that are jointly connected can be clustered into changing or moving objects by analyzing their con- nectivity.