• Corpus ID: 6322767

Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project

  title={Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project},
  author={Zhimeng Zhang and Jianan Wu and Xuan Zhang and Chi Zhang},
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated multi-camera tracking benchmark which makes great progress in this field. This report is dedicated to briefly introduce our method on DukeMTMC and show that simple hierarchical clustering with well-trained person re-identification features can get good results on this dataset. 

Features for Multi-target Multi-camera Tracking and Re-identification

This work examines the correlation between good Re-ID and good MTMCT scores, and performs ablation studies to elucidate the contributions of the main components of the system.

Multi-Target Multi-Camera Tracking Based On Mutual Information-Temporal Weight Aggregation Person Re-Identification

  • Jiayue LiY. Piao
  • Computer Science
    2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT)
  • 2022
The deep learning framework Pytorch is used to build network models and tune model-related parameters and compare experiments with existing models on the dataset to show that the proposed person re-identification model has better tracking performance.

Multiple Fisheye Camera Tracking via Real-Time Feature Clustering

This paper proposes a low-cost online tracking algorithm, namely, Deep Multi-Fisheye-Camera Tracking (DeepMFCT) to identify the customers and locate the corresponding positions from multiple overlapping fisheYE cameras.

Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach

Novel Single-Stage Global Association Tracking approaches to associate one or more detection from multi-cameras with tracked objects to solve fragment-tracking issues caused by inconsistent 3D object detection.

Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments

This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs) such as indoor multi-camera environments and uses a Kanade–Lucas–Tomasi algorithm-based frame-skipping method to reduce the computational overhead in object detection.

Improving Performance of DeepCC Tracker by Background Comparison and Trajectory Refinement

This work proposes a method to measure the similarity between detected bounding box and its original background avoiding false detection caused by OpenPose, and designs a strategy to correct the tracking trajectories which are affected by the unreliability of the correlation matrix clustering method proposed by DeepCC.

DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

A new Dynamic Graph Model with Link Prediction (DyGLIP) approach 1 is proposed to solve the data association task in Multi-Camera Multiple Object Tracking and offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions.

Multi-Target Multi-Camera Tracking by Tracklet-to-Target Assignment

This paper solves the cross-camera tracklet matching problem by TRACklet-to-Target Assignment (TRACTA), and proposes the Restricted Non-negative Matrix Factorization (RNMF) algorithm to compute the optimal assignment solution that meets a set of constraints, which should be in force in practice.

The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation

A mod for GTA V to record a MTMCT dataset has been developed and used toRecord a simulated M TMCT dataset called Multi Camera Track Auto (MTA), which contains over 2,800 person identities, 6 cameras and a video length of over 100 minutes per camera.



Multi-camera Multi-Object Tracking

This work model's the tracking problem as a global graph, and adopts Generalized Maximum Multi Clique optimization problem as the core algorithm to take both across frame and across camera data correlation into account all together.

Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

The current trends and weaknesses of multiple people tracking methods are shown, and pointers of what researchers should be focusing on to push the field forward are provided.

Fast R-CNN

  • Ross B. Girshick
  • Computer Science, Environmental Science
    2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep

Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features

It is shown how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm, which allows many different kinds of simple features to be combined into a single similarity function.

The Hungarian method for the assignment problem

  • H. Kuhn
  • Economics
    50 Years of Integer Programming
  • 2010
This paper has been presented with the Best Paper Award. It will appear in print in Volume 52, No. 1, February 2005.