The capability to track individuals in CCTV cameras is important for surveillance and forensics alike. However, it is
laborious to do over multiple cameras. Therefore, an automated system is desirable. In literature several methods have
been proposed, but their robustness against varying viewpoints and illumination is limited. Hence performance in
realistic settings is also limited. In this paper, we present a novel method for the automatic re-identification of persons in
video from surveillance cameras in a realistic setting. The method is computationally efficient, robust to a wide variety
of viewpoints and illumination, simple to implement and it requires no training. We compare the performance of our
method to several state-of-the-art methods on a publically available dataset that contains the variety of viewpoints and
illumination to allow benchmarking. The results indicate that our method shows good performance and enables a human
operator to track persons five times faster.
Video cameras are widely used for monitoring public areas, such as train stations, airports and shopping centers. When
crowds are dense, automatically tracking individuals becomes a challenging task. We propose a new tracker which
employs a particle filter tracking framework, where the state transition model is estimated by an optical-flow algorithm.
In this way, the state transition model directly uses the motion dynamics across the scene, which is better than the
traditional way of a pre-defined dynamic model. Our result shows that the proposed tracker performs better on different
tracking challenges compared with the state-of-the-art trackers, while also improving on the quality of the result.
We present a fully implemented system based on generic document knowledge for detecting the logical structure of documents for which only general layout information is assumed. In particular, we focus on detecting the reading order. Our system integrates components based on computer vision, artificial intelligence, and natural language processing techniques. The prominent feature of our framework is its ability to handle documents from heterogeneous collections. The system has been evaluated on a standard collection of documents to measure the quality of the reading order detection. Experimental results for each component and the system as a whole are presented and discussed in detail. The performance of the system is promising, especially when considering the diversity of the document collection.
We discuss measurement of properties in digitized images. We give an overview of the most accurate as well as practical feature estimation methods, particularly of geometry measurement on straight lines and circular arcs. The theory offered here gives an upper bound to the accuracy of measurement and characterization of any figure due to digitization.
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