We present a novel algorithm (which we call "FragTrack")

We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.

We revisit the problem of specific object recognition using color distributions. In some applications--such as specific person identification--it is highly likely that the color distributions will be multimodal and hence contain a special structure. Although the color distribution changes under different lighting conditions, some aspects of its structure turn out to be invariants. We refer to this structure as an intradistribution structure, and show that it is invariant under a wide range of imaging conditions while being discriminative enough to be practical. Our signature uses shape context descriptors to represent the intradistribution structure. Assuming the widely used diagonal model, we validate that our signature is invariant under certain illumination changes. Experimentally, we use color information as the only cue to obtain good recognition performance on publicly available databases covering both indoor and outdoor conditions. Combining our approach with the complementary covariance descriptor, we demonstrate results exceeding the state-of-the-art performance on the challenging VIPeR and CAVIAR4REID databases.

No abstract

Hypertext users often suffer from the "lost in hyperspace" problem: disorientation from too many Jumps while traversing a complex network. One solution to this problem is Improved authoring to create more comprehensible structures. This paper proposes several authoring tools, based on hypertext structure analysis. In many hypertext systems authors are encouraged to create hierarchical structures, but when writing, the hierarchy is lost because of the inclusion of cross-reference links. The fu-st part of this paper looks at ways of recovering lost hierarchies and finding new ones, offering authors different views of the same hypertext The second part helps authors by Identifying properties of the hypertext document Multiple metrics are developed including compactness and stratum. Compactness indicates the mtrinslc connectedness of the hypertext, and stratum reveals to what degree the hypertext is organized so that some nodes must be read before others. Several exmting hypertext are used to illustrate the benefits of each techmque. The collection of techmques provides a multifaceted view of the hypertext, which should allow authors to reduce undesired structural complexity and create documents that readers can traverse more easdy,