Pedestrian Tracking Algorithm Research Articles

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Privacy-preserving pedestrian tracking with path image inpainting and 3D point cloud features

Tracking pedestrian flow in large public areas is vital, yet ensuring privacy is paramount. Traditional visual-based tracking systems are raising concerns for potentially obtaining persistent and permanent identifiers that can compromise individual identities. Moreover, in areas such as the vicinity of restrooms, any form of data acquisition capturing human behavior should be refrained from, making it also crucial to appropriately address and complement these blind spots for a comprehensive analysis of pedestrian movement in the entire area. In this paper, we present our pedestrian tracking algorithm using distributed 3D LiDARs (Light Detection and Ranging), which capture pedestrians as 3D point clouds, omitting identifiable features. Our system bridges blind spots by leveraging historical movement data and 3D point cloud features, complemented by a generative diffusion model to predict trajectories in unseen areas. In a large-scale testbed with 70 LiDARs, the system achieved a 0.98 F-measure, highlighting its potential as a leading privacy-preserving tracking solution.

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications.

Low-light pedestrian detection and tracking algorithm based on autoencoder structure and improved Bytetrack

Low-light pedestrian detection and tracking algorithm based on autoencoder structure and improved Bytetrack

A Multistate Pedestrian Target Recognition and Tracking Algorithm in Public Places Based on Camshift Algorithm

A Multistate Pedestrian Target Recognition and Tracking Algorithm in Public Places Based on Camshift Algorithm

An Intelligent Pedestrian Tracking Algorithm Based on Sparse Models in Urban Road Scene

An Intelligent Pedestrian Tracking Algorithm Based on Sparse Models in Urban Road Scene

Pedestrian Tracking Algorithm for Video Surveillance Based on Lightweight Convolutional Neural Network

Pedestrian Tracking Algorithm for Video Surveillance Based on Lightweight Convolutional Neural Network

Robot Real-time Pedestrian Tracking Algorithm based on Deep Learning

In recent years, deep learning models have greatly improved the performance of pedestrian detection and tracking. Although the accuracy of neural network-based modeling methods is high, they often require large computational and storage resource overheads, which makes them difficult to apply to robots with high resource requirements. Pedestrian tracking for robots still faces great challenges in the problems of occlusion, multi-target, and target loss. In this thesis, we focus on solving the problem of real-time pedestrian tracking by lightweight fusion of deep learning target detection models for robots, firstly, through the lightweight YOLO network, we perform pedestrian detection and feature extraction, and then we propose a Gaussian mixture model based feature matching method to construct the target pedestrian tracker, and finally, we use the PID-based control algorithm for real-time control of the robot's motion, and finally realize the real-time pedestrian Tracking. In this thesis, we validate the feature matching method based on the Gaussian mixture model on the ETH dataset, and at the same time, combined with the motion control algorithm, we carry out the actual validation, and the experimental results show that our proposed method can realize the real-time pedestrian tracking.

DOMOPT: A Detection-Based Online Multi-Object Pedestrian Tracking Network for Videos

Due to the problem of low tracking accuracy and weak tracking stability of current multi-object pedestrian tracking algorithms in complex scenes for videos, a Detection-based Online Multi-Object Pedestrian Tracking (DOMOPT) network is proposed. First, a Multi-Level Feature Fusion (MLFF) pedestrian detection network is proposed based on the Center and Scale Prediction (CSP) algorithm. The pyramid convolutional neural network is used as the backbone to enhance the feature extraction capability for small objects. The shallow features and deep features at multiple levels are integrated to fully obtain the position and semantic information to further improve the detection performance for small objects. Then, on the basis of Joint Detection and Embedding (JDE) architecture, a Multi-Branch Pedestrian Appearance (MBPA) feature extraction network is proposed and added into the pedestrian detection network to extract the appearance feature vector corresponding to each pedestrian. The pedestrian appearance feature extraction is treated as a classification task jointly training with the pedestrian detection task, using the multi-task learning strategy. Experimental results show that the proposed network has better tracking accuracy and stability compared with state-of-the-art algorithms.

Computer vision-based framework for pedestrian movement direction recognition

Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Qmeasure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines.

Detector–Tracker Integration Framework for Autonomous Vehicles Pedestrian Tracking

Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable appearance characteristics of pedestrians and the frequent occlusion interaction, which leads to the insufficient accuracy and stability of tracking. Therefore, this paper proposes a detector–tracker integration framework for autonomous vehicle pedestrian tracking. Firstly, a pedestrian objects detector based on the improved YOLOv7 network was established. Space-to-Depth convolution layer was adopted to improve the backbone network of YOLOv7. Then, a novel appearance feature extraction network is proposed, which integrates the convolutional structural re-parameterization idea to construct a full-scale feature extraction block, which is the optimized DeepSORT tracker. Finally, experiments were carried out on MOT17 and MOT20 public datasets and driving video sequences, and the tracking performance of the proposed framework was evaluated by comparing it with the most advanced multi-object tracking algorithms. Quantitative analysis results show that the framework has high tracking accuracy. Compared with DeepSORT, MOTA improves by 2.3% in the MOT17 dataset and MOTA improves by 4.2% in the MOT20 dataset. Through qualitative evaluation on real driving video sequences, the framework proposed in this paper is robust in a variety of climate environments, and can be effectively applied to the pedestrian tracking of autonomous vehicles.

Subtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classification

In this work, open-loop position tracking using low-cost inertial measurement units is aided by Takagi-Sugeno velocity classification using the subtractive clustering algorithm to help generate the fuzzy rule base. Using the grid search approach, a suitable window of classified velocity vectors was obtained and then integrated to generate trajectory segments. Using publicly available experimental data, the reconstruction accuracy of the method is compared against four competitive pedestrian tracking algorithms. The comparison on selected test data, has demonstrated more competitive relative and absolute trajectory error metrics. The proposed method in this paper is also verified on an independent experimental data set. Unlike the methods which use deep learning, the proposed method has shown to be transparent (fuzzy rule base). Lastly, a sensitivity analysis of the velocity classification models to perturbations from the training orientation at test time is investigated, to guide developers of such data-driven algorithms on the granularity required in an ensemble modeling approach. The accuracy and transparency of the approach may positively influence applications requiring low-cost inertial position tracking such as augmented reality headsets for emergency responders.

Lightweight high-precision pedestrian tracking algorithm in complex occlusion scenarios

Lightweight high-precision pedestrian tracking algorithm in complex occlusion scenarios

Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks

The typical Internet of Things application, unattended driving systems, will need the ability to recognize relevant traffic participants and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. This article develops a high-accuracy multiple pedestrian tracking algorithm which is vital for intelligent transportation. Here, we use the off-the-shelf detectors and explore the benefits of modeling pedestrian interactions, such as the interaction of two pedestrians simultaneously matched to two pedestrians in another frame, for robust detection association. Explicitly studying interactions is nontrivial. Previous works often manually selected interacting detections (or “tracklets”) to simplify the association process. In this article, we propose a novel association method based on deep graph convolutional affinity networks (DGCANs) and extend detection-level interactions to the association-level, which treats a potential association of a detection pair as a node in the graph, and explicitly modeling the interactions among potential associations. Specifically, with the novel node, two corresponding edges are readily designed to model the compatible and colliding interactions between related associations. Our proposed method, by redefining nodes and edges, enables us to blend sufficient interaction cues from appearance and motion and learns a robust affinity measure in an end-to-end fashion. Using the Hungarian algorithm as an online tracker, our method archives state-of-the-art performance on benchmark data sets 2-D MOT15, MOT16, and MOT17.

Multitarget Pedestrian Tracking Algorithm Based on a Contour Template with Multiscale Stability

In order to overcome the problems of high error rate and poor tracking effect of traditional tracking algorithms, a multitarget pedestrian tracking algorithm based on a contour template is designed in this paper. The pedestrian template is divided into several contour regions by template voting strategy and pedestrian features are classified by regional feature similarity classification. Then, the score function is selected to realize the multiobjective pedestrian feature division, and the multicontour template matching is realized according to the similarity of deformation diversity. According to the matching results, the detection and tracking of multitarget objects are realized. The experimental results show that the feature recognition accuracy of the algorithm is between 93% and 98%, the tracking error of pedestrian gravity center position is always below 5%, and the tracking time is always below 5 S in different video frame scales, which fully proves the effectiveness of the algorithm.

Improved DeepSORT Algorithm Based on Multi-Feature Fusion

At present, the detection-based pedestrian multi-target tracking algorithm is widely used in artificial intelligence, unmanned driving cars, virtual reality and other fields, and has achieved good tracking results. The traditional DeepSORT algorithm mainly tracks multiple pedestrian targets continuously, and can keep the ID unchanged. The applicability and tracking accuracy of the algorithm need to be further improved during tracking. In order to improve the tracking accuracy of the DeepSORT method, we propose a novel algorithm by revising the IOU distance metric in the matching process and integrating Feature Pyramid Network (FPN) and multi-layer pedestrian appearance features. The improved algorithm is verified on the public MOT-16 dataset, and the tracking accuracy of the algorithm is improved by 4.1%.

Online real-time pedestrian tracking from medium altitude aerial footage with camera motion cancellation

Using medium altitude footage in various image based pedestrian detection and tracking tasks have several advantages as opposed to low altitude footage. For instance, much larger covered area, or weaker perspective distortions and occlusions. Furthermore, it does not pose privacy issues. However, it also has a disadvantage that is the irregular camera motion. Such motions can be observed with some ground level footage as well. Some state-of-the-art multi-object tracking (MOT) algorithms, which excel at pedestrian tracking, handle these implicitly, however this is insufficient in the case of medium altitude footage. The problem is further enhanced in online real-time tracking where frames may need to be skipped. Although there are explicit frame registration methods developed for wide area motion imagery (WAMI), they operate on assumptions that are not true in medium altitude footage such as the background is mostly stationary. We show that by augmenting the pedestrian tracking algorithms with a simple visual object tracking (VOT) based frame registration method their accuracy on medium altitude footage can be substantially improved without hindering the execution speed. Moreover, we show that the algorithms’ execution speed can be significantly increased by better utilization of the resources. To evaluate these, we introduce helicopter video sequences with MOT annotation.

Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism

In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.

Pedestrian Street-Cross Action Recognition in Monocular Far Infrared Sequences

The early recognition and understanding of the actions performed by pedestrians in traffic scenes leads to an anticipation of pedestrian intentions in advance and helps in the process of collision warning and avoidance in the context of autonomous vehicles. An environment with low visibility conditions such as night-time, fog, heavy rain or smoke increases the number of difficult situations in traffic. A complete and original model for assessing if a pedestrian is engaged in a street cross action using only infrared monocular scene perception is proposed in this paper. The assessment of a street cross action is done by the time series analysis of features like: pedestrian motion, position of pedestrians with respect to the drivable area and their distance with respect to the ego-vehicle. The extraction of these features emerges from the combination of a deep learning based pedestrian detector with an original tracking algorithm, a semantic segmentation of the road surface and a time series long-short term memory network based action recognition. In order to validate the proposed method we introduce a new dataset named CROSSIR. It is formed of pedestrian annotations, action annotations and semantic labels for the road. The CROSSIR dataset is suitable for several common computer vision algorithms: (1) pedestrian detection and tracking algorithms because each pedestrian has a unique identifier over the frames in which it appears; (2) pedestrian action recognition; (3) semantic segmentation of the road pixels in the infrared image.