Object Tracking in Video Images based on Image Segmentation and Pattern Matching

The moving object tracking in video pictures [1] has attracted a great deal of interest in computer vision. For object recognition, navigation systems and surveillance systems [10], object tracking is an indispensable first-step. We propose a novel algorithm for object tracking in video pictures, based on image segmentation and pattern matching [1]. With the image segmentation, we can detect all objects in images no matter whether they are moving or not. Using image segmentation results of successive frames, we exploit pattern matching in a simple feature space for tracking of the objects. Consequently, the proposed algorithm can be applied to multiple moving and still objects even in the case of a moving camera. We describe the algorithm in detail and perform simulation experiments on object tracking which verify the tracking algorithm‘s efficiency. VLSI implementation of the proposed algorithm is possible. The conventional approach to object tracking is based on the difference between the current image and the background image. However, algorithms based on the difference image cannot simultaneously detect still objects. Furthermore, they cannot be applied to the case of a moving camera. Algorithms including the camera motion information have been proposed previously, but, they still contain problems in separating the information from the background. The proposed algorithm, consisting of four stages i.e. image segmentation, feature extraction as well as object tracking and motion vector determination [12]. Here Image Segmentation is done in 3 ways and the efficiency of the tracking is compared in these three ways, the segmentation techniques used are ―Fuzzy C means clustering using Particle Swarm Optimization [5],[6],[17]”, ”Otsu’s global thresholding [16]”, ”Histogram based thresholding by manual threshold selection”, after image segmentation the features of each object are taken and Pattern Matching [10],[11],[20] algorithm is run on consecutive frames of video sequence, so that the pattern of extracted features is matched in the next frame , the motion of the object from reference frame to present frame is calculated in both X and Y directions, the mask is moved in the image accordingly, hence the moving object in the video sequences will be tracked

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Segmentation and tracking of video objects for a content-based video indexing context

This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with aPeer ReviewedPostprint (published version

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.Comment: To appear in CVPR 201