Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

Visual Attention Guided Object Detection and Tracking

2016 ◽  
pp. 99-114
Author(s):  
Debi Prosad Dogra
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Attention ◽  
Visual Saliency ◽  
Video Object ◽  
Salient Region Detection ◽  
Region Detection ◽  
Video Object Tracking ◽  
Detection And Tracking ◽  
Pros And Cons

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

scholarly journals 0494 Sleep In The Real World: A Computer Vision System To Passively Monitor Sleep And Sleep Disorders On A Continuous Basis

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A186-A186
Author(s):  
L Schneider ◽  
D Janssens ◽  
P Silberschatz ◽  
A Londeree ◽  
E Shapiro
Keyword(s):  
Computer Vision ◽  
Sleep Disorders ◽  
Real World ◽  
Vision System ◽  
Computer Vision System ◽  
The Real ◽  
Continuous Basis

scholarly journals Pros and cons of using a computer vision system for color evaluation of meat and meat products

2019 ◽  
Vol 333 ◽  
pp. 012008
Author(s):  
B Milovanovic ◽  
I Djekic ◽  
V Djordjevic ◽  
V Tomovic ◽  
F Barba ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Meat Products ◽  
Computer Vision System ◽  
Pros And Cons

scholarly journals DETECTION AND TRACKING OF MOVING OBJECTS WITH REAL-TIME ONBOARD VISION SYSTEM

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 67-71 ◽  
Author(s):  
D. Y. Erokhin ◽  
A. B. Feldman ◽  
S. E. Korepanov
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Video Sequence ◽  
Moving Objects ◽  
Vision System ◽  
Unmanned Aircraft ◽  
Computer Vision System ◽  
Geometric Transformations ◽  
Detection And Tracking ◽  
Video Sensor

Detection of moving objects in video sequence received from moving video sensor is a one of the most important problem in computer vision. The main purpose of this work is developing set of algorithms, which can detect and track moving objects in real time computer vision system. This set includes three main parts: the algorithm for estimation and compensation of geometric transformations of images, an algorithm for detection of moving objects, an algorithm to tracking of the detected objects and prediction their position. The results can be claimed to create onboard vision systems of aircraft, including those relating to small and unmanned aircraft.

scholarly journals Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 27
Author(s):  
Jessica Cooper ◽  
Ognjen Arandjelović
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Research Effort ◽  
Practical Interest ◽  
Semantic Content ◽  
Real World Data ◽  
Data Set ◽  
Novel Approach ◽  
Semantic Concepts ◽  
Ancient Coin

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

scholarly journals Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 8 ◽  
Author(s):  
Jessica Cooper ◽  
Ognjen Arandjelović
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Research Effort ◽  
Practical Interest ◽  
Semantic Content ◽  
Real World Data ◽  
Data Set ◽  
Novel Approach ◽  
Semantic Concepts ◽  
Ancient Coin

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

scholarly journals Managing caching strategies for stream reasoning with reinforcement learning

2020 ◽  
Vol 20 (5) ◽  
pp. 625-640
Author(s):  
CARMINE DODARO ◽  
THOMAS EITER ◽  
PAUL OGRIS ◽  
KONSTANTIN SCHEKOTIHIN
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Performance Improvements ◽  
Novel Approach ◽  
Stream Reasoning ◽  
Significant Performance ◽  
Intelligent Management ◽  
Truth Maintenance ◽  
Constraint Learning ◽  
Real World Problems

AbstractEfficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios.

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