dc.description.abstract | Automated security surveillance systems need to develop, not only can save money on labor costs, but also for monitoring by the security environment is a major upgrade. In this dissertation, we discuss three application issues of camera-based surveillance in various environments: abnormal posture detection, automatic detection and tracking and multicopter autonomous tracking. These three research issues are respectively discussed as follows.
In various general environments, the aged or disabled people may fall, squat, or sit down on ground due to sick or dysfunction in move. If the events are unable discovered in time, the fatal danger may be then caused. To reduce the fatal events, an environmental monitoring system for the aged or disabled people is proposed. In this study, four kinds of abnormal behavior detection are considered in the proposed system: (i) fall down and crouch, (ii) going out a room without permission, (iii) leaving specified area without permission, and (iv) recording walk trajectory. An omni-directional camera is used to capture images for monitoring. The background subtraction method is first applied to extract targets. Then principal component analysis (PCA) and the change of height of a personal trapezoidal bounding box are used to detect fall down and crouch postures. Based on the proposed method, the experiments and evaluations in a variety of environments have been carried out. The experiments results show that the PCA method can perform sixty frames per second, and the height of the patient trapezoidal bounding box method can perform eighty frames per second. The detection rate of the fall down and crouch detection using PCA is 93%, and the detection rate of that using the height of the patient trapezoidal bounding box is 92%; the detection rate of leaving ward is 95%; and the detection rate of leaving bed is 95%. Stable detection results were obtained to show the feasibility of the proposed system.
The proposed abnormal posture detection method has the following properties: (i) using an omni-directional camera can easily take 360-degree surround images, (ii) using principal component analysis (PCA) to detect the main direction and length of the personal body, (iii) using the change of the height of the patient trapezoidal bounding box and then determine the personal situation.
The view scope of a single camera is finite and limited by scene structures. Multi-fisheye cameras can monitor a wide area and trace a complete trajectory of a moving object. In this study, an automatic detection and tracking system with two fisheye cameras for environment surveillance is proposed. The proposed system is composed of two major modules: foreground detection and foreground tracking. The background subtraction method is first applied to extract targets. Then use Kalman filtering for pedestrian motion prediction. A transform table is pre-established to associate multi-cameras data in the overlapping areas. When object across disjoint camera views, the data in the lookup table can provide enough information to realize the moving object in camera views actually belonging to the same object, and keep consistent labels on the object. To improve the reliability of the tracking performance, motion and color appearance features are used to match the detected objects in different cameras. The experiments results show that the average sensitivity is 96.7% and the average false positive rate is 0.45% because the foreground objects are similar to the background. The average sensitivity rises to 98.55% with the Kalman filter. It demonstrates that the proposed method can work well under challenging conditions, such as light change, shadow interference, object occlusion.
The proposed automatic detection and tracking method has the following properties: (i) proposed foreground detection and tracking method can adapt to brightness changes and noise, (ii) using H-S color distribution map comparison and distance similarity comparison to detection, (iii) using the camera overlapping features, multi-cameras coordinate conversion, continuous tracking of objects.
A multicopter is equipped by a passive tracking device to follow a specified target. However, if want to track a non-controlled target, the passive tracking device is failed. We propose a vision-based tracking system for multicopters, system used computer vision method to track any target without additional tracking devices. In this study, propose scale candidate graphs and scale tables to improve kernelized correlation filters (KCF). There are also stable results when the scale changes. The proposed an adaptable scaled KCF algorithm, when the KCF tracking failed, a feature-based matching detector is used to re-detect the target. In experiments, there are two cases of scaling and occluded targets in the videos to be evaluated. The experimental results reveal that the proposal method can adapt the change of scale and occlusion of targets. In addition to the above ability, the tracking rate, accuracy, and execution speed are also evaluated. The experiments results show that the proposed algorithm performs speed to 26 fps, tracking rate in the scaled cases can reach 88%, tracking rate in occluded cases can reach 98%, and overlap rate can reach 87%. With trade-off between speed and accuracy, we sacrifice the execution speed of the algorithm to exchange for more robust tracking. Several experiments on various scene based on the proposed approach were conducted and evaluated.
The proposed an adaptable scaled KCF method has the following properties: (i) allows multi-axis aircraft to have the ability to track non-specific targets through computer vision tracking, (ii) using adaptable scaled KCF can adapt to the target change size and get the correct target image, (iii) using matching points to find the target position and scale to successfully retrieve the target. | en_US |