Object tracking: A survey

By Alper Yilmaz, Omar Javed, Mubarak Shah. CSUR, 2006

5,724 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/caa0fd34e50bb417fae3ee32f667e78fe5b198bc

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking (2009, background): “Many researchers have conducted studies on the first step, and general surveys can be found in recent review papers [6,7], in this step, people always use the configuration in the current frame and a dynamic model to predict the next configuration [8,9].”
A survey on activity recognition and behavior understanding in video surveillance (2013, neutral): “Yilmaz 06 Object tracking: A survey [216]”
Robust Visual Tracking via Structured Multi-Task Sparse Learning (2012, methodology): “For a more thorough survey of tracking methods, we refer the readers to Yilmaz et al. (2006) .”
Self-Paced Learning for Long-Term Tracking (2013, methodology): “We refer the reader to the survey from [9] for a broad description of related work.”
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters (2019, background): “Visual tracking [36, 25, 24] is a fundamental computer vision problem and has many realistic applications including video surveillance, behavior analysis, to name a few.”
Triplet Loss in Siamese Network for Object Tracking (2018, background): “Object tracking containing single object tracking [8, 9] and multi-object tracking [24, 25] remains an important problem with many applications, such as automated surveillance, and vehicle navigation [34].”
Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014, background): “Visual tracking is one of the most active research topics due to its wide range of applications such as motion analysis, activity recognition, surveillance, and human-computer interaction, to name a few [29].”
Fast Online Object Tracking and Segmentation: A Unifying Approach (2018, background): “Given the location of an arbitrary target of interest in the first frame of a video, the aim of visual object tracking is to estimate its position in all the subsequent frames with the best possible accuracy [69, 65, 55].”
Minimum error bounded efficient ℓ1 tracker with occlusion detection (2011, background): “A thorough review can be found in [25].”
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild (2018, background): “Note that the maximum sequence length is 30, thus t ∈ [1 , 30].”
Robust visual tracking using ℓ1 minimization (2009, background): “The challenges in designing a robust visual tracking algorithm are caused by the presence of noise, occlusion, varying viewpoints, background clutter, and illumination changes [32].”
Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories (2011, methodology): “Many tracking-based methods are used or could be adapted for trajectory acquisition (for a comprehensive review on tracking techniques, readers may refer to relevant surveys [19]).”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “• on-line tracking of the movements of a person or an object of interest [70, 100, 137];”
Visual tracking decomposition (2010, background): “5(e)(f), which are relatively robust to the illumination changes [19].”
Multi-cue Correlation Filters for Robust Visual Tracking (2018, background): “Although substantial progress has been made in the past decades [52, 16, 35], there still remain many challenges [49].”
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis (2013, methodology): “Vision-based tracking is taken one step further using feature-based tracking [145].”
Spatiotemporal Saliency in Dynamic Scenes (2010, methodology): “In computer vision, spatiotemporal saliency and the related task of background subtraction are commonly used as a preprocessing step for object and event detection [10], activity and gesture recognition [29], tracking [30], surveillance [12], and video retrieval [27].”
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”