A mobile vision system for robust multi-person tracking

By Andreas Ess, Bastian Leibe, Konrad Schindler, Luc Van Gool. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

800 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7047496e6159cdac94b2871516b593bf1c6bf0c5

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Citations in the corpus

Real-Time Pedestrian Detection with Deep Network Cascades (2015, methodology): “Pedestrian detection has been an important problem for decades, given its relevance to a number of applications in robotics, including driver assistance systems [8], road scene understanding [16] or surveillance systems.”
The Seventh Visual Object Tracking VOT2019 Challenge Results (2019, background): “A number of datasets in RGB+depth (RGBD) tracking focus on pedestrian and hand tracking [23, 85, 14, 30] and object pose estimation for robotics [13, 80].”
BiCov: a novel image representation for person re-identification and face verification (2012, methodology): “The proposed representation has been experimentally validated on two datasets for person re-identification (VIPeR [10] and ETHZ [6, 20]) and one for face verification (LFW [12]).”
MOT16: A Benchmark for Multi-Object Tracking (2016, neutral): “7 moving medium sunny [13] MOT16-07 30 1920x1080 500 (00:17) 55 16,322 32.”
Effective Fusion Factor in FPN for Tiny Object Detection (2020, methodology): “FreeAnchor and RetinaNet are improved versions with building FPN using the P2, P3, P4, P5, P6 and adjusting anchor size to [8, 16, 32, 64, 128].”
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking (2020, methodology): “Some videos in ETH also appear in the testing set of the MOT17 which are removed from the training dataset for fair comparison.”
Survey of Pedestrian Detection for Advanced Driver Assistance Systems (2010, methodology): “Gammeter et al. [124] perform multibody tracking by combining the implicit shape model detector [103] and the stereo-odometry-based tracker of [29].”
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking (2020, methodology): “We classify the sequences according to: We classify the sequences according to: – Moving or static camera the camera can be held by a person, placed on a stroller (Ess et al. 2008) or on a car (Geiger et al. 2012), or can be positioned ﬁxed in the scene.”
Human detection using partial least squares analysis (2009, methodology): “[8] also considers sequence #1 in their experiments, so we have added their results in Figure 6(c).”
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies (2017, background): “It covers a wide range of applications such as sports analysis [40, 47, 70], biology (birds [41], ants [27], ﬁshes [62, 63, 14], cells [42, 36]), robot navigation [11, 12], and autonomous driving vehicles [13, 53].”
Multi-cue onboard pedestrian detection (2009, background): “While Ess et al. [11] extract the ground plane from a depth map and fuse it with detections in a graphical model, Ess et al. [12] add further 3D scene information by integration with Structure-from-Motion.”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “Other datasets focus exclusively on pedestrian annotations on images [20, 25, 79, 24, 88, 23, 58].”
The Fastest Pedestrian Detector in the West (2010, methodology): “Evaluation scripts, detector descriptions, additional results (including on the ETH dataset [11] and under varying conditions) are all available online at [1].”
Towards Real-Time Multi-Object Tracking (2019, methodology): “The first category includes the ETH dataset (Ess et al. 2008) and the CityPersons (CP) dataset (Zhang, Benenson, and Schiele 2017).”
Rethinking the Competition Between Detection and ReID in Multiobject Tracking (2020, methodology): “For a fair comparison, we use the same training datasets as the baseline tracker JDE [8], consisting of ETH [30], CityPerson [31], CalTech [32], MOT17 [33], CUDK-SYSU [34] and PRW [35].”
Predictive Inequity in Object Detection (2019, background): “…introduced in the computer vision com-1 munity for developing methods for recognizing pedestrians at a variety of distances (Geiger et al., 2012; Ess et al., 2008; Doll´ar et al., 2012) and for recognizing all objects in road scenes relevant to the autonomous driving task (Cordts et al., 2016;…”