Kinect depth sensor for computer vision applications in autonomous vehicles

Eric, Niyonsaba; Jang, Jong-Wook

doi:10.1109/icufn.2017.7993842

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…Instead, they rely on other cues to estimate depth, such as object size, perspective, and shadows. Moreover, some articles proposed adding RFID [10] as a complement to the surveillance camera for distance estimation, or additional sensors such as a LiDAR sensor [11] or Kinect sensor [12]. Given that our system employs a single camera for capturing maritime traffic and adding a secondary sensor is not possible, our research narrows its focus to monocular camera distance estimation methods.…”

Section: Related Workmentioning

confidence: 99%

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Petković,

Vujović

2023

JMSE

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Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.

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Section: Related Workmentioning

confidence: 99%

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Petković,

Vujović

2023

JMSE

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“…Therefore, the ToF camera is more commonly used as a data acquisition device. Since the depth cameras work well in low light and even in dark conditions, object recognition based on obtained 3D images has been used in many scenes, such as human detection, industrial assembly, gesture recognition, and others [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Luna et al [ 29 ] presented a new method for detecting people only using depth images, and the data was captured by a depth camera in a frontal position.…”

Section: Introductionmentioning

confidence: 99%

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

Wang

et al. 2023

Sensors

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Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

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“…Kinect, LIDAR, SONAR, optical flow and stereo camera sensors are widely used for depth estimation (see [12], [13]) and hence these can be potentially used for obstacle avoidance as well without resorting to computation-intensive approaches like SLAM and SfM. However these sophisticated sensors are expensive and add unnecessary burden to the UAV in terms of weight as well as consumption of power.…”

Section: Introductionmentioning

confidence: 99%

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

Singla

Padakandla

Bhatnagar

2021

IEEE Trans. Intell. Transport. Syst.

185

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This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground vehicular robots. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) and autonomous exploration which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered during navigation without collisions. In addition, our technique has a high inference rate (a key factor in robotic applications) and is energy-efficient as it minimizes oscillatory motion of UAV and reduces power wastage.

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Kinect depth sensor for computer vision applications in autonomous vehicles

Cited by 18 publications

References 12 publications

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

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