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Object Tracking

Datasets

Dataset Description
KITTI Tracking Multi-object tracking dataset, taken from a moving vehicle with the viewpoint of the tracker
MOT Challenge Multi-pedestrian tracking dataset captured from surveillance cameras
DETRAC challenging real-world multi-object detection and multi-object tracking benchmark
CAVIAR A number of video clips recorded acting out the different scenarios
TUD Datasets Includes "TUD Multiview Pedestrians" and "TUD Stadtmitte" Sequences
PETS2009 A large crowd dataset focusing on multi-pedestrian tracking and counting
EPFL Multi-camera Dataset Multi-camera multi-pedestrian tracking videos
ETHZ Sequences Inner city sequences captured by mobile platforms
PSU-HUB Sequences Multi-pedetrian tracking videos captured in the PSU student union building

Recent Papers

Paper Description Rank
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking CVPR2019 69
MOTS: Multi-Object Tracking and Segmentation CVPR2019 -
Multiple People Tracking using Body and Joint Detections CVPRW2019 13
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification arxiv 2019 1
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking arxiv 2019 16
Exploit the Connectivity: Multi-Object Tracking with TrackletNet arxiv 2019 17
Features for Multi-Target Multi-Camera Tracking and Re-Identification CVPR2018 -
Online Multi-Object Tracking with Dual Matching Attention Networks ECCV2018 45
Collaborative Deep Reinforcement Learning for Multi-Object Tracking ECCV2018 -
Multi-object Tracking with Neural Gating Using Bilinear LSTM ECCV2018 51
Art Track: Articulated Multi-Person Tracking in the Wild CVPR2017 -
Deep Network Flow for Multi-Object Tracking CVPR2017 -
Multi-Object Tracking with Quadruplet Convolutional Neural Networks CVPR2017 -
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies ICCV2017 -
Non-Markovian Globally Consistent Multi-Object Tracking ICCV2017 -
Detect to Track and Track to Detect ICCV2017 -
Online Multi-Object Tracking Using CNN-Based Single Object Tracker With Spatial-Temporal Attention Mechanism ICCV2017 -

Papers from dataset site

Paper Description
CAR from KITTI
Near-Online Multi-Target Tracking With Aggregated Local Flow Descriptor ICCV2015
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking ICRA2018
Learning to Track: Online Multi-Object Tracking by Decision Making ICCV2015
Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios IEEE Transactions on Intelligent Transportation Systems 2019
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering 2018 IEEE Intelligent Vehicles Symposium
A Lightweight Online Multiple Object Vehicle Tracking Method 2018 IEEE Intelligent Vehicles Symposium
Multi-class Multi-object Tracking Using Changing Point Detection ECCV2016
Learning Optimal Parameters For Multi-target Tracking BMVC2015
End-to-end Learning of Multi-sensor 3D Tracking by Detection ICRA2018
Online Multi-Object Tracking via Structural Constraint Event Aggregation CVPR2016
Combined image- and world-space tracking in traffic scenes ICRA2017
FollowMe: Efficient Online Min-Cost Flow Tracking With Bounded Memory and Computation ICCV2015
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking CVPR2013
Bayesian Multi-object Tracking Using Motion Context from Multiple Objects 2015 IEEE Winter Conference on Applications of Computer Vision
Online Domain Adaptation for Multi-Object Tracking BMVC2015
PEDESTRIAN from KITTI
Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle Sensors 2019
from MOT
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Online Multi-Object Tracking with Dual Matching Attention Networks ECCV2018
Fusion of Head and Full-Body Detectors for Multi-Object Tracking CVRPW2018
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification ICME2018
Multiple Hypothesis Tracking Revisited ICCV2015
Iterative Multiple Hypothesis Tracking with Tracklet-level Association IEEE Transactions on Circuits and Systems for Video Technology
Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks sensors 2018
Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering AVSS2018
Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning IEEE Access
Multi-object Tracking with Neural Gating Using Bilinear LSTM ECCV2018
High-Speed Tracking-by-Detection Without Using Image Information AVSS2017
Multiple Object Tracking via Feature Pyramid Siamese Networks IEEE Acecess 2018
Online Multi-target Tracking with Strong and Weak Detections ECCV2016
Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking AVSS2017
Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data AVSS2017
from arxiv
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D
Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation
Long-Term Visual Object Tracking Benchmark
Fast Online Object Tracking and Segmentation: A Unifying Approach
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

github

survey papers

Evaluation Measures

Measure Perfect Description
Avg Rank 1 This is the rank of each tracker averaged over all present evaluation measures.
MOTA 100% Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP 100% Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes.
IDF1 100% ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
FAF 0 The average number of false alarms per frame.
MT 100% Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML 0% Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
FP 0 The total number of false positives.
FN 0 The total number of false negatives (missed targets).
ID Sw. 0 The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3].
Frag 0 The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz Inf Processing speed (in frames per second excluding the detector) on the benchmark.

Dataset summary

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