Spatial Depth Information Acquisition of Monocular Video Object in Traffic Monitoring Scene

2020 ◽  
Author(s):  
Yuhua Sun ◽  
Gong Zhang ◽  
Binqian Wu
Keyword(s):  
Information Acquisition ◽  
Traffic Monitoring ◽  
Depth Information ◽  
Video Object ◽  
Monocular Video ◽  
Spatial Depth

Video Object Segmentation

2009 ◽  
pp. 809-816 ◽  
Author(s):  
Ee Ping Ong ◽  
Weisi Lin
Keyword(s):  
Object Segmentation ◽  
Research Effort ◽  
Complete Solution ◽  
Cost Savings ◽  
Traffic Monitoring ◽  
Video Object Segmentation ◽  
Video Object ◽  
Home Office ◽  
Practical Applications ◽  
Object Based

Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.

scholarly journals Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2679
Author(s):  
Zhoujing Ye ◽  
Guannan Yan ◽  
Ya Wei ◽  
Bin Zhou ◽  
Ning Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Monitoring System ◽  
Information Acquisition ◽  
Vibration Signal ◽  
High Energy ◽  
Traffic Monitoring ◽  
Vibration Monitoring ◽  
Traffic Information ◽  
Real Time Traffic

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.

scholarly journals Online supervised attention-based recurrent depth estimation from monocular video

2020 ◽  
Vol 6 ◽  
pp. e317
Author(s):  
Dmitrii Maslov ◽  
Ilya Makarov
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Temporal Information ◽  
Depth Information ◽  
Safe Driving ◽  
Monocular Video ◽  
Depth Reconstruction

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.

scholarly journals Sculpture 3D Modeling Method Based on Image Sequence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaofei Liu
Keyword(s):  
Three Dimensional ◽  
Basic Feature ◽  
Image Sequence ◽  
Depth Information ◽  
Structure Estimation ◽  
Pose Tracking ◽  
Mesh Model ◽  
Restoration Algorithm ◽  
Monocular Video ◽  
Posture Tracking

This thesis first introduces the basic principles of model-based image sequence coding technology, then discusses in detail the specific steps in various implementation algorithms, and proposes a basic feature point calibration required in three-dimensional motion and structure estimation. This is a simple and effective solution. Aiming at the monocular video image sequence obtained by only one camera, this paper introduces the 3D model of the sculpture building into the pose tracking framework to provide initial depth information. The whole posture tracking framework can be divided into three parts, namely, the construction of the initial sculpture model, the posture tracking between frames, and the robustness processing during continuous tracking. In order to reduce the complexity of calculation, this paper proposes a new three-dimensional mesh model and a moving image restoration algorithm based on this model. At the same time, the influence of the intensity and direction factors in the scene is added, the simulation results are given, and the next step is discussed. The optimization work that needs to be done.

scholarly journals Dronar—Geoforensic Search Sonar from a Drone

2021 ◽  
Vol 1 (3) ◽  
pp. 202-212
Author(s):  
Alastair Ruffell ◽  
Amy Lally ◽  
Benjamin Rocke
Keyword(s):  
Weather Conditions ◽  
Depth Information ◽  
Adverse Weather Conditions ◽  
Water Weed ◽  
Sediment Types ◽  
Adverse Weather ◽  
Aerial Vehicle ◽  
Spatial Depth ◽  
Reconnaissance Surveys

Lightweight sonar devices may be tethered to an unmanned aerial vehicle or drone and quickly deployed over water for real-time imaging in 2D and the on site creation of geolocated, interactive bathymetric maps without the need for a boat. We show how such data is useful in the preliminary stages of water searches, by providing geophysicists, hydrologists and divers with spatial depth information, the distribution of dive and equipment hazards such as entanglement objects (weed, discarded items) and sediment types. One bathymetry case study location is described in detail, with a further two summarized to demonstrate reconnaissance surveys. Limitations of drone-based sonar surveys are outlined, including dense water weed cover; limits on flight times and adverse weather conditions.

Polarization-Based Depth Information Acquisition of Underwater Target

2015 ◽  
Vol 22 (8) ◽  
pp. 43-47 ◽  
Author(s):  
赵泓扬 ZHAO Hong-yang ◽  
姚文卿 YAO Wen-qing
Keyword(s):  
Information Acquisition ◽  
Depth Information ◽  
Underwater Target
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