Study On Video-based Target Detection And Tracking

Video-based target detection and tracking is one of the research hotspots in the field of computer vision. It plays a very important role in many applications, such as smart surveillance, human-machine interface and visual navigation. This thesis aims at researching the technologies of target detection and tracking, which focuses on the key issues on motion detection, matching and framework of tracking.The main contributions of this dissertation are listed as follows:â‘ A new method named 3D OGHM (Orthogonal Gaussian-Hermite Moments) for motion detection is proposed. This thesis considers the motion detection at the point of all dimensions of the video space. By combining the temporal and the special characteristics of the video sequences, the 3D OGHM algorithm is proposed. As one of TVA (Temporal Variation Analysis) methods for motion detection, it has high efficiency firstly. Additionally, because the 3D property of video is considered, it has a stronger ability to enhance the motion information in comparison with the congener methods. Besides, it is outstanding in anti-jamming as well.â‘¡A new version of geometric active contour modal (ACM) named IMDC (Intensity-based Model with Distance Constraint) is proposed. Most of current ACMs either have to re-initialize the level set function constantly or require the gradient flow to stop the evolution of the curve. To solve this problem, the internal energy term of IMDC imports a distance constraint that penalizes the deviation of the level set function from a signed distance function (SDF). And the external energy term of IMDC adopts the intensity instead of the gradient of the image to drive the curve on zero level set toward the desired image features, such as the object boundaries. The experimental results show that the model presented efficiently avoids the re-initialization and overcomes the problem that the traditional models can not work well with the images with low gradient. Moreover, our model is able to acquire the global optimization of the segmentation and it has a good anti-noise performance. The initialization is also simple and flexible as well.â‘¢An improved affine image alignment (AIA) algorithm called ADC (Active Drift Correction) is proposed. To solve the traditional problem of worse compatibility between the robustness and the efficiency of AIA. The basic idea of ADC is to incorporate a drift correction term into the traditional goal energy function, which also called"component goal energy thought"(CGET). Based on CGET, ADC has the ability of anti-drift, which boosts its robustness. Moreover, many extra techniques (e.g. dividing blocks or second tracking) in traditional methods for a high robustness are unnecessary. The experimental results show that our algorithm is simple and efficient. It achieves a higher performance of robustness than the traditional methods. However, it makes no compromise with the complexity and real time performance of the algorithm. Besides, the dissertation analyzes and researches the update strategies of AIA.â‘£A mixed framework (MF) for target detection and tracking is introduced. By combining the independent algorithms proposed in this thesis, a mixed framework (MF) for video-based target detection and tracking is introduced. This framework which includes two sub-frameworks emphasizing respectively on the detection and the match can be used in the case of fixed or moving background. It also fits these cases that multiple targets or special (known) target should be detected or tracked. Specially, in the DE (Detection Emphasized) sub-framework, local OGHM instead of global 3D OGHM is adopted for higher detection efficiency. And a new method named BM (Bidirectional Map) is introduced to resolve the target recognition and matching problems. The results of the MF research and experiments futher show that the algorithms proposed in this thesis are effective.