Object detection and tracking is one of the most important and challenging branches in computer vision, and have been widely applied in various fields, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With the rapid development of deep learning (DL) networks and GPU’s computing power, the performance of object detectors and trackers has been greatly improved. To understand the main development status of object detection and tracking pipeline thoroughly, in this survey, we have critically analyzed the existing DL network-based methods of object detection and tracking and described various benchmark datasets. This includes the recent development in granulated DL models. Primarily, we have provided a comprehensive overview of a variety of both generic object detection and specific object detection models. We have enlisted various comparative results for obtaining the best detector, tracker, and their combination. Moreover, we have listed the traditional and new applications of object detection and tracking showing its developmental trends. Finally, challenging issues, including the relevance of granular computing, in the said domain are elaborated as a future scope of research, together with some concerns. An extensive bibliography is also provided.

Deep learning in multi-object detection and tracking: state of the art

Application of machine learning techniques for predicting the consequences of construction accidents in China

Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis

A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that our model outperformed most previous studies.

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study.

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data

In this article, two new models, namely granulated RCNN (G-RCNN) and multi-class deep SORT (MCD-SORT), for object detection and tracking, respectively from videos are developed. Object detection has two stages: object localization (region of interest RoI) and classification. G-RCNN is an improved version of the well-known Fast RCNN and Faster RCNN for extracting RoIs by incorporating the unique concept of granulation in a deep convolutional neural network. Granulation with spatio-temporal information enables more accurate extraction of RoIs (object regions) in unsupervised mode. Compared to Fast and Faster RCNNs, G-RCNN uses (i) granules (clusters) formed over the pooling feature map, instead of its all feature values, in defining RoIs, (ii) only the positive RoIs during training, instead of the whole RoI-map, (iii) videos directly as input, rather than static images, and (iv) only the objects in RoIs, instead of the entire feature map, for performing object classification. All these lead to the improvement in real-time detection speed and accuracy. MCD-SORT is an advanced form of the popular Deep SORT. In MCD-SORT, the searching for association of objects with trajectories is restricted only within the same categories. This increases the performance in multi-class tracking. These characteristic features have been demonstrated over 37 videos containing single-class, two-class, and multi-class objects. Superiority of the models over several state-of-the-art methodologies is also established extensively, both qualitatively and quantitatively.

Anima Pramanik

Papers

Deep learning in multi-object detection and tracking: state of the art

Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data

Granulated RCNN and Multi-Class Deep SORT for Multi-Object Detection and Tracking

A real-time video surveillance system for traffic pre-events detection.

RT-GSOM: Rough tolerance growing self-organizing map