Research Of Several Key Techniques In Intelligent Video Systems

Motivated by today’s complex security situation and urgent application requirements, intelligent video technology, as a new method for security supervision, has become a hotspot in both scientific research and engineering technology. Intelligent video surveillance technology combines traditional video surveillance techniques with modern pattern recognition and graphics/image processing algorithms, it automatically detects and tracks objects by analyzing the video stream without any manual intervention. Even more, behavior understanding and assistant decision-making can be achieved based on the objects’information obtained from detection/tracking module. Intelligent video surveillance technology brings a Man-Not-in-the-Loop automatic monitoring, which leads to a revolution in video surveillance technology.Although the standard flow of intelligent video has been proposed and the core algorithm has also been developed for a long time, the application of intelligent video technology still faces some difficulties:current algorithms are vulnerable to a variety of interfering factors such as illumination changing in real environment, the object’s special moving pattern as well as its indistinguishable surface may also lead to a sharp decline in performance. Therefore, researchers are still looking forward to more effective and stable algorithms which are capable of precisely dealing with complex background and special motion patterns in object detection and tracking. In this dissertation, core algorithms in intelligent video system such as moving objects detection and tracking are researched, the research work in this dissertation mainly includes the following aspects:1. Aiming at the problem that existing GMM algorithm prone to get broken target when dealing with slowly moving object, a moving object detection algorithm based on foreground model matching and short-term stability measure was proposed. Potential foreground models that generated at each pixel were used to match the incoming pixel and a matching mechanism was developed to update the foreground models. Meanwhile, the short-term stability measure was employed to deal with foreground component which was fluctuating in its neighboring region. Experimental results showed that the combination of foreground matching and short-term stability measure enhanced the adaptability to slowly moving objects.2. An abandoned object detection algorithm based on foreground model matching and short-term stability measure was proposed. Abandoned object is the extreme case of slowly moving object. The proposed algorithm used foreground models to characterize the abandoned object, and ensure their the highest priority to firstly match the following pixel values rather than background model s, reducing the risk of foreground pixel mismatching the background models and hence prevent the abandoned objects being absorbed into background. Abandoned object detection results under multiple scenarios verified the feasibility of the proposed algorithm.3. A sixteen-channel ICA based motion detection algorithm using four observation vector generation methods was proposed. Independent component analysis and principal component analysis were typical multi-dimensional statistical signal analysis methods. The existing moving object detection algorithm based on independent component analysis and principal component analysis using just single method for observation vector generation and two-channel data for separation, unable to provide more effective information for separation, and hence resulting in incomplete detection result. A large number of comparative experiments shown the difference on detection performance under different observation signal generation methods and different channel numbers, and validated the improvement by selecting larger channel number and using four different observation signal generation methods. The characteristic differences between statistical methods and a variety of other traditional methods are also verified in the experiments.4. Aiming at existing Meanshift based object tracking algorithm using fixed quantization order to generate a histogram, an improved Meanshift object tracking algorithm using adaptive quantization order in color space was proposed. Quantization order was dynamically changed according to the size changing of the moving objects, and DTW was employed to measure the similarity of features with different length. Comparative experiments demonstrated that the improved algorithm improved the calculation flexibility and achieved a lower average per-frame time consuming.5. As an engineering application of above theories, an intelligent video surveillance system was designed and implemented based on a set of core intelligent video algorithms. In accordance with the general flow of intelligent video processing, the detailed development process of each module were given and an intelligent video surveillance system which can be put into practical use was integrated based on the individual modules. The system realized many real-time multi-channel intelligent monitoring functions such as virtual alert line setting, intrusion alarm, key frame preservation, invasive video playback and integrated environmental parameters displaying. It well demonstrated the specific functions and application value of intelligent video surveillance system.