Research Of Object Detection And Tracking Algorithm Based On Machine Learning

Intelligent video surveillance is a valuable research in machine vision field, and is widely applied in the field of aviation, traffic and public security, etc. As the most basic component in video surveillance, object detection and tracking has attracted more and more attention. Whether a tracking is successful or not mainly depends on the degree in which an object is segmented from its surroundings. In this dissertation, the problem of detection and tracking has been treated as a binary classification, and efficient and accurate tracking can be realized by suitable features and the design of discriminative classifier.However, object detection and tracking is a very challenge task due to target appearance variability, which is the primary restricting element, including pose variation and shape deformation, as well as illumination change, camera motion, viewpoint change, and occlusions. The problems above mentioned have been studied in this paper in a machine learning point of view, in which detection and tracking is regarded as a binary classification problem.The thesis consists of two parts as followed. The first part is the description of target model. In order to represent the target model comprehensively and effectively, several features are analyzed in-depth such as Scale Invariable Feature Transform (SIFT), PCA-SIFT, SURF (Speed Up Robust Feature), and DAISY etc. The merits and drawbacks of each feature are expounded respectively, and their application scenarios are indicated. Difference of Gaussian in muti-scale space is selected for feature points detection. Considering invariant to image rotation, scale, change in viewpoint, change in illumination, and partial occlusion,DAISY feature descriptor is chosen to describe the object image.The second part is the design of classifier. Following the discussion of random forest which is popular in recent years, the thesis has elaborated Hough Forest (HF). Hough Forest combines random forest and hough transform, and serves as classifier to separate the target from background. Hough forest classifier is trained by image partial patches and their labels, and the structure parameters of Hough forest are generated to classify the object patches and background patches. The set of leaf nodes of each tree in the Hough forest can be regarded as a discriminative codebook. In order to improve the adaptability of the target model, the paper proposes to update the discriminative codebook by computing the similarity measurement between the detection results of the video coming in sequence and the codebook.Experiment results show that the proposed algorithm, combining efficient DAISY feature descriptor and robust discriminative Hough Forest which, has satisfactory tracking precision and good real time performance, and works well under the condition of partial occlusions, different image resolutions, and complex scenes.