Christian Micheloni

TL;DR: The proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring, based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories.

TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.

TL;DR: In this paper, a slice convolution block is introduced to capture vertical food traits that are common to a large number of categories (i.e., 15% of the whole data in current datasets).

TL;DR: In this article, the low-level image and video processing techniques needed to implement a modern surveillance system are described and change detection methods for both fixed and mobile cameras (pan and tilt) are introduced and registration methods for multicamera systems with overlapping and nonoverlapping views are discussed.