Visual analysis of human behavior is a broad field within computer vision. In this field of work, we are interested in dynamic methods in the analysis of crowd behavior which consist in detecting the abnormal entities in a group in a dense scene. These scenes are characterized by the presence of a great number of people in the camera’s field of vision. The major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by human operators. We present in this paper a new approach for the detection of dynamic anomalies of very dense scenes measuring the speed of both the individuals and the whole group. The various anomalies are detected by dynamically switching between two approaches: An artificial neural network (ANN) for the management of group anomalies of people, and a DensityBased Spatial Clustering of Application with Noise (DBSCAN) in the case of entities. For greater robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions. The two latter phases have proven that the results of the simulation are comparable to existing work.

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Dynamic detection of abnormalities in video analysis of crowd behavior with DBSCAN and neural networks

Framework for estimating distance and dimension attributes of pedestrians in real-time environments using monocular camera

The analysis of the human behavior from video is a wide field of the vision by computer. In this work we are interested in the analysis of the crowd behavior and its entities in a dense scene. These scenes are characterized by the presence of a great number of people in the camera's field of vision. A major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by operators. We present in this paper a new approach for the anomalies detection very dense scenes relaying on the speed of both the individuals and the whole group. The various anomalies are detected by switching in a dynamic way between two approaches: the artificial neurons networks “ANN” for the management of group anomalies of people, and the Density Based Spatial Clustering of Application with Noise “DBSCAN” in the case of entities. For more robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions.

Dynamic detection of anomalies in crowd's behavior analysis

In recent years, people have paid more attention to the shadow detection of moving target in video surveillance system. The shadow will directly affect the accuracy of moving target recognition, and do adverse effects on the subsequent processing such as target segmentation, target extraction and target tracking. So shadow elimination is an important part of intelligent video surveillance system. The paper proposes an efficient method of shadow elimination based on HSV color space and image region information. The proposed method uses the shadow elimination algorithm based on HSV color space to obtain the shadow regions to be dealt with. Then combining with the image region information, it judges whether the four boundary points of the moving target are shadow points. Eliminate the connected regions which shadow points exist from outer to inner along the shadow direction. It can avoid the regions whose color features are close to shadow inside the target object to be mistakenly eliminated. The experimental results show that the proposed method significantly improves the quality of shadow elimination.

An Efficient Method of Shadow Elimination Based on Image Region Information in HSV Color Space

Manually identifying similarity between any images is a bit difficult task. So, we have come up with an image similarity detection model which will identify the similarities between two images. Features of the one image are compared with the other to find how similar they are. The scale-invariant feature transform (SIFT) algorithm is used to detect similarity between input images and also to calculate the similarity score up to which extent the images are matched. SIFT detects the keypoints and computes its descriptors. We will find the best matches of the descriptors by using FLANN-based algorithm. It takes the descriptor of first image and compares with second image. The accuracy point which we achieved in translational image similarity is 60% and in rotational image similarity is 90%, and feature matching similarity differs depending upon the given inputs.

Detecting Image Similarity Using SIFT

The shadows of moving objects have great influence on the accuracy and effectiveness of objects tracking and behavior recognition, this paper proposes an elimination method based on Gaussian Mixture Model (GMM) First, we improve the adaptability of GMM by making learning rate change with the speed of the moving object to eliminate ghost Then, we come up with a shadow elimination method based on normalized RGB space and segment shadows by their characteristics of brightness, color and the spatial relationship between shadows and moving objects At last, under different light and projecting surfaces, we take a large number of experiments of moving objects, showing the method of this paper has good adaptability and robustness

A shadow detection method based on improved Gaussian Mixture Model

SIFT(Scale Invariant Feature Transform) algorithm is a widely used method in image matching for its good invariance to image translation, rotation and illumination changes. However, due to local similarity of some images, when matching the SIFT feature points, some false matches occur. In this paper, an improved SIFT matching algorithm based on geometric similarity is proposed. For correct matches, there exists a constant similarity ratio. So based on it, we can eliminate false matching points which do not meet this condition. Experiments showed that the improved matching algorithm could eliminate mismatches effectively and make the feature points matching more accurate.

An improved SIFT matching algorithm based on geometric similarity

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A shadow detection method based on improved Gaussian Mixture Model

An improved SIFT matching algorithm based on geometric similarity