Object Tracking and Detecting Based on Adaptive Background Subtraction

AbstractA tracking algorithm based on adaptive background subtraction about the video detecting and tracking moving objects is presented in this paper. Firstly, we use median filter to achieve the background image of the video and denoise the sequence of video. Then we use adaptive background subtraction algorithm to detect and track the moving objects. Adaptive background updating is also realized in this paper. Finally, we improve the accuracy of tracking through open operation. The simulation results by MATLAB show that the adaptive background subtraction is useful in both detecting and tracking moving objects, and background subtraction algorithm runs more quickly

CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units

Parallel Motion Images in Visual and Near Infrared Spectrum

This paper presents parallel film recording of sunlight reflection from the same matter, in visual (V) and near infrared (Z) spectrum. We have designed a ZRGB-Motion GoPro video camera for parallel high quality motion recording in dual spectrum. Recorded are two parallel video twins, presenting different information in the grey area. The usage of extended reality in the infrared spectrum could be used in artistic as well as identification purposes. The scenography and costume designs give the technical solution in the making of a parallel motion image. The importance of parallel reproduction of motion images lies in the differences that create a new expanded space in communication design

Scalable surveillance software architecture

Copyright © 2006 IEEEVideo surveillance is a key technology for enhanced protection of facilities such as airports and power stations from various types of threat. Networks of thousands of IP-based cameras are now possible, but current surveillance methodologies become increasingly ineffective as the number of cameras grows. Constructing software that efficiently and reliably deals with networks of this size is a distributed information processing problem as much as it is a video interpretation challenge. This paper demonstrates a software architecture approach to the construction of large scale surveillance network software and explores the implications for instantiating surveillance algorithms at such a scale. A novel architecture for video surveillance is presented, and its efficacy demonstrated through application to an important class of surveillance algorithms.Henry Detmold, Anthony Dick, Katrina Falkner, David S. Munro, Anton van den Hengel, Ron Morriso

Learning object motion patterns for anomaly detection and improved object detection

We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach. 1

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.This work was partially funded by the Spanish Government DPI2013-40534-R grant and Valencian Government GV/2013/005 grant

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection