A Combined Vision-Based Multiple Object Tracking and Visual Odometry System

Aladem, Mohamed; Rawashdeh, Samir A.

doi:10.1109/jsen.2019.2937304

Cited by 18 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], a relational appearance features and motion patterns learning-based data association model was designed to generate tracks with the reference of one object and its feature differences compared to other objects. In [17], an MOT model was designed to track and detect moving objects by eliminating features lying on tracked objects. A simple, instinctive cost function was considered to streamline the real-time performance of the visual odometry system.…”

Section: Related Workmentioning

confidence: 99%

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Mekala

Viriyasitavat

Park

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms Manuscript

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Mekala

Viriyasitavat

Park

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…The IMU and GPS were located on the ground node and used to measure the attitude and position of each sensing node in a global coordinate frame. Since the KITTI [35] dataset consists of different types of sensors, the research in [1,5,8,9,44] using this dataset also fit under hybrid sensors with the primary goal of localising a vehicle. Table 4 summarises the sensors based on primary sensors and a vision sensor along with the secondary sensor that complements the primary sensor.…”

Section: Hybrid Sensorsmentioning

confidence: 99%

“…Vision is one of the primary senses that allow humans to navigate their environment. To design autonomous machines that perform human tasks such as driving [1,3,[5][6][7][8][9][10], fishing [11], agricultural activities [2], and medical diagnoses [12][13][14][15][16], computer vision can help increase productivity. The inclusion of computer vision in humancomputer interaction, robotics, and medical diagnoses provides humans with better tools for completing tasks efficiently and making decisions with better insights.…”

Section: Introductionmentioning

confidence: 99%

Object Tracking Using Computer Vision: A Review

Kadam,

Fang,

Zou

2024

Computers

View full text Add to dashboard Cite

Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines.

show abstract

“…Frame-based multi-object tracking has been well-established in the literature for quite some time. Most works currently utilize direct methods, specifically tracking-by-detection , using optimized object detectors, while focusing on the data association aspect of object tracking [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Related Workmentioning

confidence: 99%

High-Temporal-Resolution Object Detection and Tracking Using Images and Events

Shair

Rawashdeh

2022

J. Imaging

Self Cite

View full text Add to dashboard Cite

Event-based vision is an emerging field of computer vision that offers unique properties, such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes, to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution object detection and tracking approach that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple, low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze tracking performance at different temporal rates. Our approach shows promising results, with minimal performance deterioration at higher temporal resolutions (48–384 Hz) when compared with the baseline frame-based performance at 24 Hz.

show abstract

A Combined Vision-Based Multiple Object Tracking and Visual Odometry System

Cited by 18 publications

References 25 publications

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Object Tracking Using Computer Vision: A Review

High-Temporal-Resolution Object Detection and Tracking Using Images and Events

Contact Info

Product

Resources

About