This paper conducts fairness testing of automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems. We evaluate eight state-of-the-art deep learning-based pedestrian detectors across demographic groups on large-scale ...

To achieve intelligent monitoring and unmanned driving in coal mines, accurate pedestrian detection plays a crucial role. However, due to various complex factors such as uneven lighting, dense dust, cable interference, and obstacles underground in coal ...

In an effort to overcome the problem of insufficient network ability of YOLOV8 backbone feature extraction and poor feature fusion ability, a C2f module based on attention mechanism is introduced to carry to a new and higher level the model’s ability to ...

The YOLOv3-AFPN model is proposed in this paper to solve the issue of poor performance in pedestrian detection in complex scenes with small targets and occlusions. This model uses four layers of feature detection to progressively enhance the recognition ...

Pedestrian detection and path prediction are significant challenges in vision-based surveillance systems. Because of variances in pedestrian postures and scales, backdrops, and occlusion, advanced computer vision applications face many obstacles. To ...

Multispectral pedestrian detection is of great importance in various around-the-clock applications, i.e., self-driving and video surveillance. Fusing the features from RGB images and thermal infrared (TIR) images to explore the complementary information ...

A backdoor attack is executed by injecting a few poisoned samples into the training dataset of Deep Neural Networks (DNNs), enabling attackers to implant a hidden manipulation. This manipulation can be triggered during inference to exhibit controlled ...

Pedestrian detection technology, combined with techniques such as pedestrian tracking and behavior analysis, can be widely applied in fields closely related to people's lives such as traffic, security, and machine interaction. However, the multi-...

Pyroelectric infrared (PIR) sensors are popular for pedestrian detection in green campus lighting systems due to their low power consumption, low cost, and ease of installation. However, these sensors still fail to detect important information such as ...

Multispectral pedestrian detection that enables continuous (day and night) localization of pedestrians has numerous applications. Existing approaches typically aggregate multispectral features by a simple element-wise operation. However, such a local ...

Aiming at the requirement of the mobile robot for the human-robot coexisting environment to distinguish people from objects and accurately estimate pedestrian pose and speed information in the indoor environment, the pedestrian detection and tracking ...

Far-infrared pedestrian detection onboard is more challenging compared to pedestrian detection of visible light. Existing works proved that the output of RoIs extraction, named as the proposal, is significantly related to recall rate and computational ...

In this paper, we address the challenging difficulty of detecting pedestrians with variation in scale and the illumination of the images. Occurrences of pedestrians with such variations exhibit diverse features. Therefore, it intensely affects the ...

The automation of driving systems relies on proof of the correct functioning of perception. Arguing the safety of deep neural networks (DNNs) must involve quantifiable evidence. Currently, the application of DNNs suffers from an incomprehensible ...

In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes. We show the performance of the proposed system in the context of object ...

Pedestrian detection in the night surveillance is a challenging yet not largely explored task. As the success of the detector in the daytime surveillance and the convenient acquisition of all-weather data, we learn knowledge from these data to benefit ...

The spatial behavior of passersby can be critical to blind individuals to initiate interactions, preserve personal space, or practice social distancing during a pandemic. Among other use cases, wearable cameras employing computer vision can be used to ...

Non-Maximum Suppression (NMS) is essential for object detection and affects the evaluation results by incorporating False Positives (FP) and False Negatives (FN), especially in crowd occlusion scenes. In this paper, we raise the problem of weak ...

Standing in line is one of the most common social behaviors in public spaces but can be challenging for blind people. We propose an assistive system named LineChaser, which navigates a blind user to the end of a line and continuously reports the ...

Pedestrian detection is a canonical problem for safety and security applications, and it remains a challenging problem due to the highly variable lighting conditions in which pedestrians must be detected. This article investigates several domain ...