Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for...
Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point...
Object detection is important in many applications, such as autonomous driving. While 2D images lack depth information and are sensitive to environmental conditions, 3D point clouds can provide accurate depth information and a more descriptive enviro...
Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and mu...
The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to...
When measurement rates grow, most Compressive Sensing (CS) methods suffer from an increase in overheads of transmission and storage of CS measurements, while reconstruction quality degrades appreciably when measurement rates reduce. To solve these pr...
To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmen...
Multi-object tracking (MOT) is an important problem in computer vision that has a wide range of applications. Currently, object occlusion detecting is still a serious challenge in multi-object tracking tasks. In this paper, we propose a method to sim...
The rapid development of large artificial intelligence (AI) models—large language models (LLMs), multimodel large language models (MLLMs) and vision–language models (VLMs)—has enabled instruction-driven visual understanding, where a...
Multiple object tracking (MOT) is a fundamental task in vision, but MOT techniques for plenoptic video are scarce. Almost all 2D MOT algorithms that show high performance mostly use the detection-based method which has the disadvantage of operating o...
The drawback of pixel-based change detection is that it neglects the spatial correlation with neighboring pixels and has a high commission ratio. In contrast, object-based change detection (OBCD) depends on the accuracy of the segmentation scale, whi...
Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF...
Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial acce...
Multi-object tracking in video surveillance is subjected to illumination variation, blurring, motion, and similarity variations during the identification process in real-world practice. The previously proposed applications have difficulties in learni...
Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M mark...
The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste p...
Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joi...
Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data associa...
Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we conside...
To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed f...
Object detection and tracking are tasks that humans can perform effortlessly in most environments. Humans can readily recognize individual trees in forests and maintain unique identifiers during occlusion. For computers, on the other hand, this is a...
Sensing services for the detection of humans and animals by analyzing the environmental changes of wireless local area network (WLAN) signals have attracted attention in recent years. In object detection using WLAN signals, a widely known technique i...
Object detection has been playing a significant role in computer vision for a long time, but it is still full of challenges. In this paper, we propose a novel object detection framework based on relationship among different objects and the scene-leve...
In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration o...
The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (...
Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval ap...
Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, due to problems of light fluctuation, similar appearances of pigs, shap...
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater cha...
In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenari...
The idea of SLAM (Simultaneous Localization and Mapping) being a solved problem revolves around the static world assumption, even though autonomous systems are gaining environmental perception capabilities by exploiting the advances in computer visio...
In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies...
Multiple object tracking (MOT) constitutes a critical research area within the field of computer vision. The creation of robust and efficient systems, which can approximate the mechanisms of human vision, is essential to enhance the efficacy of multi...
Fruit occlusion, dense clustering, platform vibration, and cross-frame re-identification errors reduce the reliability of video-stream yield estimation in high-density dwarf apple orchards. To address these limitations, this study developed a vision-...
Recently, weakly supervised object detection (WSOD) with image-level annotation has attracted great attention in the field of computer vision. The problem is often formulated as multiple instance learning in the existing studies, which are often trap...
Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense...
Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, r...
Traditional Multiple Object Tracking (MOT) methods in satellite videos mostly follow the Detection-Based Tracking (DBT) framework. However, the DBT framework assumes that all objects are correctly recognized and localized by the detector. In practice...
The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of mov...
As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology t...
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and...
The use of drone-borne imagery for tree recognition holds high potential in forestry and ecological studies. Accurate species identification and crown delineation are essential for tasks such as species mapping and ecological assessments. In this stu...
In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today’s popular tracking methods are implemented using deep learning. But many monitoring devices do not hav...
Object detection in low-light environments is often hampered by unfavorable factors such as low brightness, low contrast, and noise, which lead to issues like missed detections and false positives. To address these challenges, this paper proposes a l...
A lightweight YOLOv8 variant, CIMB-YOLOv8, is proposed to address challenges in remote sensing object detection, such as complex backgrounds and multi-scale targets. The method enhances detection accuracy while reducing computational costs through tw...
In video surveillance, robust detection of foreground objects is usually done by subtracting a background model from the current image. Most traditional approaches use a statistical method to model the background image. Recently, deep learning has al...
This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is mode...
In complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. P...
The central vein sign (CVS) is a supportive imaging biomarker for multiple sclerosis (MS), but its manual assessment on susceptibility-weighted imaging with phase enhancement (SWIp) is time-consuming and reader-dependent. Existing automated methods r...
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A stat...
Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when...
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