Multiple object tracking: A literature review

Luo, Wenhan; Xing, Junliang; Milan, Anton; Zhang, Xiaoqin; Liu, Wei; Kim, Taekyun

doi:10.1016/j.artint.2020.103448

Cited by 555 publications

(309 citation statements)

References 36 publications

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“…Regarding MOT various techniques [19] have also been proposed by the research community. These techniques focus on addressing common challenges such as frequent occlusions, identical appearances, track management and interaction among objects.…”

Section: Related Workmentioning

confidence: 99%

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

et al. 2020

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Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment, using a novel low-resolution Thermal Vision Sensor (TVS). Specifically, the research presents the development and implementation of a two-step framework, consisting of a Computer Vision (CV) based method to detect and track multiple occupants combined with Convolutional Neural Network (CNN) based HAR. The proposed algorithm uses frame-difference over consecutive frames for occupant detection, a set of morphological operations to refine identified objects, and features are extracted before applying a Kalman filter for tracking. Laterally, a 19-layer CNN architecture is used for HAR and afterward the results from both methods are fused using time interval based sliding window. This approach is evaluated through a series of experiments based on benchmark Thermal Infrared datasets (VOT-TIR2016) and multi-occupant data collected from TVS. Results demonstrate that the proposed framework is capable of detecting and tracking 88.46% of multi-

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Section: Related Workmentioning

confidence: 99%

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

et al. 2020

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“…In addition to the object detection, multiple object tracking (MOT) techniques have been developed to keep track of the detected objects. Depending on how objects are initialized and whether or not objects are detected automatically, the MOT techniques can be divided into two groups: detection-based tracking (DBT) and detection-free tracking (DFT) ( 25 ). DBT in particular can discover new objects and terminate disappearing objects automatically, so it is more useful than DFT in tracking randomly appearing pedestrians on the image.…”

Section: Machine Learning-based Collision Warning Systemmentioning

confidence: 99%

Study on the Framework of Intersection Pedestrian Collision Warning System Considering Pedestrian Characteristics

Lee

Kim

et al. 2019

Transportation Research Record: Journal of the Transportation R

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One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian–vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian’s crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian’s crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian–vehicle collisions by capturing the pedestrian’s crossing intention.

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“…Increasing the number of objects creates a new and major difficulty in detection, data association and tracking. In MOT, special attention should be paid to determine the identity of each object at any time and to maintain the consistency of objects identities during tracking, and to solve multi identeties matching challenges [5][6][7]. Accordingly, validation criteria must be considered alongside each 1 *Corresponding Author Institutional Email: y.baleghi@nit.ac.ir (Y. Baleghi) object to perform a proper object matching during the video sequence.…”

Section: Introductionmentioning

confidence: 99%

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Dadgar

Baleghi

Ezoji

2021

IJE

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In video surveillance, multiple objects tracking (MOT) is a challenging task due to object matching problem in consecutive frames. The present paper aims to propose an improved object matching approach in MOT based on Zernike moments and combination of multiple similarity distance metrics. In this work, the object is primarily detected using background subtraction method while the Gaussian Mixture Model (GMM) is applied for object extraction in the next frames. Subsequently, the color histogram and the magnitude of Zernike moments of the objects are calculated. In the next step, the objects are matched in the current and the previous frames based on the Hausdorff distance between objects, Earth Mover's distance (EMD) between their color histograms, and Chi-square distance between their Zernike moments. Then, a voting mechanism is designed to find the best consensus object matching from the aforementioned metrics. Eventually, the location of each object is predicted by the Kalman filter to continue tracking in subsequent frames. The results show that the object tracking and matching performance is improved using the proposed method in the video sequences of the multi-camera pedestrian "EPFL" video dataset. Specifically, errors caused by the merging of targets are reduced in the proposed tracking process.

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Multiple object tracking: A literature review

Cited by 555 publications

References 36 publications

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

Study on the Framework of Intersection Pedestrian Collision Warning System Considering Pedestrian Characteristics

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

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