Key-point based tracking for illegally parked vehicle detection

This research aims to develop a target detection and tracking system that can realize real-time video surveillance. The purpose of the research is to realize a monitoring application that can run automatically and intelligently to detect and track illegally parked vehicles. Since the application scenario of the algorithm is a real traffic environment, it must be able to adapt to complex environmental interference, such as drastic changes in lighting conditions, frequent occlusion, and long-term stable tracking. The thesis shows the detailed design process and test results of the system. This algorithm combines the target detection function based on deep learning network and the multi-object tracking algorithm based on key point matching. The method shown in the thesis focuses on detecting and tracking stationary vehicles in the no parking area. An object detection algorithm based on a deep learning network is used to recognize vehicles. Once the recognized vehicle is defined as an illegally parked vehicle through the determination of its motion state and location, an algorithm based on key-point matching is developed and tracked for this type of vehicle. If the target is still stationary in the no parking area after a period, the system will generate an alarm. The method was tested in more than 20 hours of video. The video comes from public database and our own. They all show real surveillance scenes, including different time periods of the day and different locations. The test results show that the method achieves 100% in precision (also called positive predictive value), 95% in recall (also known as sensitivity) and 97% in F1 (a measure that combines precision and recall). The results obtained also produce better detection and tracking compared to other comparable methods

A distributed camera system for multi-resolution surveillance

We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments