W4: Real-Time Surveillance of People and Their Activities

By Ismail Haritaoglu, David Harwood, Larry S. Davis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

2,834 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/518597d91ed49c28f5cf3f0a0b05609568b7e084

How researchers see this paper

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

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Tracking Multiple Occluding People by Localizing on Multiple Scene Planes (2009, background): “Blob tracking is a popular low-cost approach for tracking objects [17], [16].”
Human Action Recognition With Video Data: Research and Evaluation Challenges (2014, methodology): “[86], [105], [134], [155] × × I I × × × × [24] S × I S × × × × [53], [75] × I × I × I √ × [52], [74], [78] × I × I × I × × [66] × S I I × × × × [69] × S I I × S √ × [129] × S I I × S × × [27] I S × I × S × × [14] I S × I × × × × [12] I I I × S × √ × [26], [30], [38], [42] I × I I × × √ × [35], [44] ”
Face Recognition from Long-Term Observations (2002, background): “Separately, systems for tracking people in unconstrained environments have become increasingly robust, and are able to track individuals for minutes or hours [8, 5].”
Distraction suppression for vision-based pose estimation at city scales (2013, methodology): “The methods described in this paper rely on the idea of background subtraction, which have traditionally been applied to static camera systems for surveillance operations [12], [13].”
Unsupervised Bayesian Detection of Independent Motion in Crowds (2006, background): “They, like [13, 20, 4], and [27] for the static case, have a probabilistic scheme for finding body part primitives that are likely to fit together.”
Detecting Moving Objects, Ghosts, and Shadows in Video Streams (2003, background): “Selectivity [10][2][8][1], * Shadow [4][10], * Ghost [ 1 ][3], * High-frequency • Temporal filtering [14][15][6] illumination changes • Size filtering * Sudden global [1], * illumination changes”
Silhouette Analysis-Based Gait Recognition for Human Identification (2003, methodology): “To eliminate the inaccuracy due to segmentation error, each foreground region is then tracked from frame to frame by a simple correspondence method based on the overlap of their respective bounding boxes in any two consecutive frames [23].”
Tracking and Object Classification for Automated Surveillance (2002, neutral): “W4 [3] uses dynamic appearance models to track people.”
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, methodology): “Recent literature contains several works dealing with marker-based human tracking, the most of which make use of drones [51,65,77–79, 91, 94, 96,”
Statistical modeling of complex backgrounds for foreground object detection (2004, methodology): “A variant model is used in [ 5 ], where the distribution of temporal variations in color at each pixel is used to model the spectral feature of the background.”
Pedestrian detection and tracking with night vision (2005, methodology): “Various approaches have been proposed including background elimination and analysis, periodic motion, symmetry, silhouette shape analysis of the foreground [17], [ 18 ], spatial‐temporal silhouette analysis of human parts with a three-dimensional (3-D) model [20], periodicity and self-similarity ana”
Foreground object detection from videos containing complex background (2003, methodology): “An commonly used approach to extract foreground objects from the image sequence is through background suppression, or background subtraction and its variants [ 3 , 11, 12] when the video is grabbed from a stationary camera.”
Algorithms for cooperative multisensor surveillance (2001, background): “There has been considerable interest in the area of human motion tracking in recent years [2], [6], [38], [43], [60]–[67].”
Learning semantic scene models from observing activity in visual surveillance (2005, background): “Much of previous research has focused on determining the trajectory of objects in single camera views [1], [2].”