9,614 research outputs found
Multi-Object Tracking based Roadside Parking Behavior Recognition
Roadside parking spaces can alleviate the shortage of parking spaces, but there are some shortcomings to the charges for roadside parking. The popular charging methods at present mainly include manual charging, geomagnetic detection charging, meter charging, etc. These methods have certain limitations, such as high cost, difficult deployment, and low acceptance of people. To solve the shortcomings of roadside parking charges, this thesis proposes a scheme based on deep learning and image recognition. More specifically, the thesis proposes a scheme for detecting and tracking vehicles, recognizing license plates, recognizing vehicle parking behavior, and recording vehicle parking periods through the monocular camera to solve the problem of roadside parking charges. The scheme has the advantages of convenient deployment, low labor cost, high efficiency, and high accuracy. The main work of this thesis is as follows:
1. Based on the You Only Look Once (YOLO) algorithm, this thesis proposes a trapezoidal convolution algorithm to detect objects and improve the detection efficiency for the problem that the vehicle is far and small in the image.
2. Proposes a one-stage license plate recognition scheme based on YOLO, aiming to simplify the license plate recognition process.
3. Depending on the characteristics of the vehicle, this thesis proposes a feature extraction model of the vehicle, called the horizontal and vertical separation model, which use to combine with the deep Simple Online and Real-time Tracking (SORT) object tracking framework to track the vehicle and improve the tracking efficiency.
4. Uses a Long Short-Term Memory (LSTM) model to classify the behavior of the vehicle into three types: Park, leave, and no behavior.
5. Groups these modules together, and the engineering code is debugged a lot to realize a complete Roadside Parking Behavior Recognition (RPBR) system
Vehicle Tracking Using Video Surveillance
In numerous applications including the security of individual vehicles as well as public transportation frameworks, the ability to follow or track vehicles is very helpful. Using computer vision and deep learning algorithms, the project deals with the concept of vehicle tracking in real-time based on continuous video stream from a CCTV camera to track the vehicles. The tracking system is tracking by detection paradigm. YOLOv3 object detection is applied to achieve faster object detection for real-time tracking. By implementing and improving the ideas of Deep SORT tracking for better occlusion handling, a better tracking system suitable for real-time vehicle tracking is presented. So as to demonstrate the achievability and adequacy of the framework, this chapter presents exploratory consequences of the vehicle following framework and a few encounters on handy executions
Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking
In recent decades, due to the groundbreaking improvements in machine vision,
many daily tasks are performed by computers. One of these tasks is
multiple-vehicle tracking, which is widely used in different areas such as
video surveillance and traffic monitoring. This paper focuses on introducing an
efficient novel approach with acceptable accuracy. This is achieved through an
efficient appearance and motion model based on the features extracted from each
object. For this purpose, two different approaches have been used to extract
features, i.e. features extracted from a deep neural network, and traditional
features. Then the results from these two approaches are compared with
state-of-the-art trackers. The results are obtained by executing the methods on
the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the
second method caused up to 15.9%. The proposed methods can still be improved by
extracting more distinguishable features
MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic
Currently, there are computer vision systems that help us with tasks that
would be dull for humans, such as surveillance and vehicle tracking. An
important part of this analysis is to identify traffic anomalies. An anomaly
tells us that something unusual has happened, in this case on the highway. This
paper aims to model vehicle tracking using computer vision to detect traffic
anomalies on a highway. We develop the steps of detection, tracking, and
analysis of traffic: the detection of vehicles from video of urban traffic, the
tracking of vehicles using a bipartite graph and the Convex Hull algorithm to
delimit moving areas. Finally for anomaly detection we use two data structures
to detect the beginning and end of the anomaly. The first is the QuadTree that
groups vehicles that are stopped for a long time on the road and the second
that approaches vehicles that are occluded. Experimental results show that our
method is acceptable on the Track4 test set, with an F1 score of 85.7% and a
mean squared error of 25.432.Comment: 14 pages, 14 figures, submitted to Journal of Internet Services and
Applications - JIS
Vehicle Tracking Based on Historical Intersection Over Union
Multi-object tracking (MOT) could be applied to many video analysis scenarios, such as vehicle speed estimation, vehicle re-identification, and vehicle abnormal behavior detection. A tracking task can be formulated as a data association problem, for which there exist many different types of solutions. Track-by-detection is one of the most common approaches for the MOT task. In this paradigm, the tracking algorithm relies on the detection results to decide whether detected vehicles in sequential frames belong to the same track. In our work, we developed a reliable vehicle tracker following this paradigm, while considering the balance between tracking efficiency and tracking performance. Our algorithm extends the existing intersection over union (IOU) tracker and improves upon it by fusing historical tracking information. In addition, our tracker allows tuning certain hyperparameters that lead to improved results, including the minimum confidence score, the maximum confidence score, the IOU threshold, and the length of a candidate track. We demonstrated the effectiveness and efficiency of our approach using the UA-DETRAC benchmark dataset. Our proposed approach runs at an average speed of 28 frames per second (fps), which is 16 faster than one of the baselines but 24 times slower than the other. With regard to effectiveness, however, our approach outperforms both baseline methods by more than 20% in most of the tracking performance metrics and achieves a 60% performance improvement in certain cases. We conclude that our tracker, which balances running speed and performance, could be useful for applications running in a real-time environment
Real-Time Bird's Eye View Multi-Object Tracking system based on Fast Encoders for object detection
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), September 20-23, 2020, Rhodes, Greece. Virtual Conference.This paper presents a Real-Time Bird’s Eye View
Multi Object Tracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for object
detection and a combination of Hungarian algorithm and
Bird’s Eye View (BEV) Kalman Filter, respectively used for
data association and state estimation. The system is able to
analyze 360 degrees around the ego-vehicle as well as estimate
the future trajectories of the environment objects, being the
essential input for other layers of a self-driving architecture,
such as the control or decision-making. First, our system
pipeline is described, merging the concepts of online and realtime DATMO (Deteccion and Tracking of Multiple Objects),
ROS (Robot Operating System) and Docker to enhance the
integration of the proposed MOT system in fully-autonomous
driving architectures. Second, the system pipeline is validated
using the recently proposed KITTI-3DMOT evaluation tool that
demonstrates the full strength of 3D localization and tracking
of a MOT system. Finally, a comparison of our proposal with
other state-of-the-art approaches is carried out in terms of
performance by using the mainstream metrics used on MOT
benchmarks and the recently proposed integral MOT metrics,
evaluating the performance of the tracking system over all
detection thresholds.Ministerio de Ciencia, InnovaciĂłn y UniversidadesComunidad de Madri
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
3D multi-object tracking (3D MOT) stands as a pivotal domain within
autonomous driving, experiencing a surge in scholarly interest and commercial
promise over recent years. Despite its paramount significance, 3D MOT confronts
a myriad of formidable challenges, encompassing abrupt alterations in object
appearances, pervasive occlusion, the presence of diminutive targets, data
sparsity, missed detections, and the unpredictable initiation and termination
of object motion trajectories. Countless methodologies have emerged to grapple
with these issues, yet 3D MOT endures as a formidable problem that warrants
further exploration. This paper undertakes a comprehensive examination,
assessment, and synthesis of the research landscape in this domain, remaining
attuned to the latest developments in 3D MOT while suggesting prospective
avenues for future investigation. Our exploration commences with a systematic
exposition of key facets of 3D MOT and its associated domains, including
problem delineation, classification, methodological approaches, fundamental
principles, and empirical investigations. Subsequently, we categorize these
methodologies into distinct groups, dissecting each group meticulously with
regard to its challenges, underlying rationale, progress, merits, and demerits.
Furthermore, we present a concise recapitulation of experimental metrics and
offer an overview of prevalent datasets, facilitating a quantitative comparison
for a more intuitive assessment. Lastly, our deliberations culminate in a
discussion of the prevailing research landscape, highlighting extant challenges
and charting possible directions for 3D MOT research. We present a structured
and lucid road-map to guide forthcoming endeavors in this field.Comment: 24 pages, 6 figures, 2 table
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