A survey on visual surveillance of object motion and behaviors

By Weiming Hu, Tien-Ping Tan, Liang Wang, S. Maybank. 2004

2,507 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

A system for learning statistical motion patterns (2006, background): “In this section, the state of the art in behavior understanding [15], [17] is reviewed.”
Multiple object tracking: A literature review (2014, methodology): “• The first set [19], [20], [21], [23], [24] discusses tracking as an individual part while this work specifically discusses various aspects of MOT.”
Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations (2010, methodology): “One way is using cameras to visually detect people’s motion [8, 7].”
Crowded Scene Analysis: A Survey (2015, methodology): “Motion patterns learning is important in automated visual surveillance [5], [6], [21].”
Action Tubelet Detector for Spatio-Temporal Action Localization (2017, neutral): “video surveillance [10, 21] or video captioning [33, 37].”
Object tracking using SIFT features and mean shift (2009, background): “The goal of object tracking is to determine the position of the object in images continuously and reliably against dynamic scenes [1].”
Multimodal Human Computer Interaction: A Survey (2005, background): “Extensive surveys have been previously published in several areas such as face detection [190][63], face recognition [196], facial expression analysis [47][131], vocal emotion [119][109], gesture recognition [96][174][136], human motion analysis [65][182][182][56][3][46][107], audio-visual automatic”
Learning to Recognize Activities from the Wrong View Point (2008, background): “There is a long tradition of research on interpreting activities in the vision community (see, for example, the extensive surveys in [18,20]).”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, background): “This term is equivalent to other terms in the literature including “behavior” [12], “activity” [18], “action” [19], and”
A Novel Performance Evaluation Methodology for Single-Target Trackers (2015, methodology): “Index Terms—Performance analysis, single-target tracking, model-free tracking, tracker evaluation methodology, tracker evaluation datasets, tracker evaluation system F”
A survey of video datasets for human action and activity recognition (2013, background): “…Enrique J. Carmona a, Antonio Fernández-Caballero b a Dpto. de Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain b Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La…”
A survey of vision-based methods for action representation, segmentation and recognition (2011, background): “F R + E N G Vision, Perception and Multimedia Understanding”
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance (2008, methodology): “The label of the prototype with minimal distance indicates the best matching path [28], [49], [67].”
Trajectory-Based Anomalous Event Detection (2008, neutral): “[2] in the field of video surveillance.”