Does Depth Estimation Help Object Detection?

2022 ◽  
Author(s):  
Bedrettin Cetinkaya ◽  
Sinan Kalkan ◽  
Emre Akbas
Keyword(s):  
Object Detection ◽  
Depth Estimation

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

scholarly journals Task-Aware Monocular Depth Estimation for 3D Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12257-12264 ◽  
Author(s):  
Xinlong Wang ◽  
Wei Yin ◽  
Tao Kong ◽  
Yuning Jiang ◽  
Lei Li ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Depth Estimation ◽  
3D Perception ◽  
Research Attention ◽  
3D Object ◽  
Depth Prediction ◽  
Monocular Depth ◽  
Almost All ◽  
3D Object Detection

Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions (“things and stuff”) in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D object recognition and localization. To date how to boost the depth prediction accuracy of foreground objects is rarely discussed. In this paper, we first analyze the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground and background depth using separate optimization objectives and decoders. Our method significantly improves the depth estimation performance on foreground objects. Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new state-of-the-art results among other monocular methods. Code will be available at: https://github.com/WXinlong/ForeSeE.

scholarly journals Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 10478-10485 ◽  
Author(s):  
Yingjie Cai ◽  
Buyu Li ◽  
Zeyu Jiao ◽  
Hongsheng Li ◽  
Xingyu Zeng ◽  
...  
Keyword(s):  
Object Detection ◽  
Depth Estimation ◽  
Physical World ◽  
Depth Information ◽  
Detection Accuracy ◽  
Unified Framework ◽  
3D Object ◽  
Depth Recovery ◽  
Bounding Boxes ◽  
3D Object Detection

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on account of absence of depth information, this paper proposes a novel unified framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. Different from the widely studied 2D bounding boxes, the proposed novel structured polygon in the 2D image consists of several projected surfaces of the target object. Compared to the widely-used 3D bounding box proposals, it is shown to be a better representation for 3D detection. In order to inversely project the predicted 2D structured polygon to a cuboid in the 3D physical world, the following depth recovery task uses the object height prior to complete the inverse projection transformation with the given camera projection matrix. Moreover, a fine-grained 3D box refinement scheme is proposed to further rectify the 3D detection results. Experiments are conducted on the challenging KITTI benchmark, in which our method achieves state-of-the-art detection accuracy.

scholarly journals Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection

2021 ◽  
Author(s):  
N.-A.-M. Mai ◽  
P. Duthon ◽  
L. Khoudour ◽  
A. Crouzil ◽  
S. A. Velastin
Keyword(s):  
Object Detection ◽  
Depth Estimation ◽  
3D Object ◽  
3D Object Detection

scholarly journals Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

2019 ◽  
Author(s):  
Yan Wang ◽  
Wei-Lun Chao ◽  
Divyansh Garg ◽  
Bharath Hariharan ◽  
Mark Campbell ◽  
...  
Keyword(s):  
Object Detection ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Visual Depth ◽  
3D Object ◽  
3D Object Detection

Energy-efficient Airflow Control for Human Thermal Comfort using Object Detection and Monocular Depth Estimation

2021 ◽  
Author(s):  
Phurich Saengthong ◽  
Chalermpol Charnsripinyo ◽  
Seksan Laitrakun
Keyword(s):  
Object Detection ◽  
Thermal Comfort ◽  
Energy Efficient ◽  
Depth Estimation ◽  
Human Thermal Comfort ◽  
Monocular Depth
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