Background subtraction using variable threshold RGB model

Background subtraction is an essential step in video processing since it extracts the region of interest from an arbitrary image. So, it reduces computational complexity. It also helps in proper implementation of algorithms for further processing as per requirement. This paper describes a simple method to extract the foreground. The algorithm used, works well for indoor operation

Detection of vehicle occlusion using a generalized deformable model

This paper presents a vehicle occlusion detection algorithm based on a generalized deformable model. A 3D solid cuboid model with up to six vertices is employed to fit any vehicle images, by varying the vertices for a best fit. The advantage of using such a model is that the number of parameterized vertices is small which can be easily deformed. Occlusion is detected by recording the changes in the Area Ratio and the dimensions of the generalized deformable model. Our tests show that the new modeling algorithm is effective in detecting vehicle occlusion.published_or_final_versio

Multiple object tracking using a neural cost function

This paper presents a new approach to the tracking of multiple objects in CCTV surveillance using a combination of simple neural cost functions based on Self-Organizing Maps, and a greedy assignment algorithm. Using a reference standard data set and an exhaustive search algorithm for benchmarking, we show that the cost function plays the most significant role in realizing high levels of performance. The neural cost function’s context-sensitive treatment of appearance, change of appearance and trajectory yield better tracking than a simple, explicitly designed cost function. The algorithm matches 98.8% of objects to within 15 pixels

Motion Tracking With Web Camera Images Based On Spatial Properties

Machine Vision provides a cheap and flexible mean of tracking objects in motion when implemented by a web Camera. The low resolution digital images, capturing the different instances of the scene of object in motion yields information which can be used to lay a historical track of the object. The implementation of such a system. involved the separation of the objects from the background using threshold segmentation techniques. Although it accepted the variation of natural lighting, it assumed that the background was lighter than the objects. By that method, the objects which have the potential to move, were separated from the stationary background.The segmentation scheme implemented was a robust automated scheme, and form the preprocessing stage of the whole system

Vehicle-type identification through automated virtual loop assignment and block-based direction biased motion estimation

This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for estimation accuracy; and the number of virtual loops per lane is large. Comparing this with traditional ILD, there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy and fully utilize computing resources. Second, there is no failure rate associated with the virtual loops and installation and maintenance cost can be kept to a minimum. Third, virtual loops may be re-allocated anywhere on the frame, giving flexibility in detecting different parameters.published_or_final_versio

Multi-view Vehicle Detection based on Part Model with Active Learning

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods

Animal Detection using Background Subtraction & Blob Detection Technique

Animal detection plays an important role in day to day life due to its impact on the human life directly or indirectly. In the area like an airport where the presence of any kind of animal is strictly restricted, animal detection tool can play an important role in such areas. In this work, the performance of different image features and classification algorithms in animal detection application, and design a real-time animal detection system following criteria in terms of accuracy, time and cost of computation is explored. To follow these qualities, detection process is done in two levels. In first level, bulb detection process is used to subtract background from the image and this image is used in second stage for finding the region of the object using regionpropos algorithm. To examine the animal detection system, we created our own dataset, this dataset can be updated according to the application or use. The result of the approach shows that we can successfully detect the animal when it comes in a particular background

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction