Cost-Effective Vehicle Type Recognition in Surveillance Images With Deep Active Learning and Web Data

Huang, Yue; Liu, Zhenwei; Jiang, Minghui; Yu, Xian; Ding, Xinghao

doi:10.1109/tits.2018.2888698

Cited by 39 publications

(9 citation statements)

References 19 publications

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“…When a vehicle stops or passes over the loop the oscillation frequency increases, so the period decreases. This parameter ∆T gives us the amplitude of the vehicle inductive signature at a time instant [49]. Acceleration of the vehicle will cause the wheelbase to seem smaller [50], While deceleration will cause the wheelbase to seem larger according to the classification system.…”

Section: Research Resultsmentioning

confidence: 99%

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Dewi¹

2021

IJEM

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The background of this research raises the problem of the development of smart transportation in terms of monitoring and enforcement of traffic on the highway, with the proposed system that will help many parties such as the police and the government. The system that is running is to develop a system using CCTV that is placed on every corner of the capital city to replace the police in carrying out road surveillance and law enforcement against lawbreakers, especially in the traffic sector. The method used in this research is by using literature review of many previous research journals, by reading many journals will be able to add knowledge and can deepen ongoing research. The problem raised in this research is finding solutions to problems in the transportation sector, especially smart transportation, using smart transportation will be able to connect all systems that have been made. This research produces a system proposal that can be used in further research and can be applied in terms of the development of smart cities, especially smart transportation.

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Section: Research Resultsmentioning

confidence: 99%

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Dewi¹

2021

IJEM

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“…AL has been also considered in various applications of intelligent transportation systems. For instance, the authors in [294] investigate the vehicle type recognition problem, in which labeling a sufficient amount of data in surveillance images is very time consuming. To tackle this problem, this work leveraged fully labeled web data to decrease the required labeling time of surveillance images using deep transfer learning.…”

Section: Vic2 Applications Of Almentioning

confidence: 99%

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Baccour,

Mhaisen,

Abdellatif

et al. 2021

Preprint

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-oflife. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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“…Storage of vehicle data on the Cloud system will make it easier for the data to be processed, and used as data on vehicle service using the Cloud system, so all vehicle data will be neatly stored and there can be known deficiencies of the vehicle [44], and can be repaired if a deficiency is found and and can be repaired immediately by therefore neat data will make everything easier [45].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Greedy Algorithm on Traffic Violation Enforcement

Dewi¹,

Putra²

2021

IJEME

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The background of this research is how the application of the algorithm in traffic control systems, with the Greedy algorithm, the system will decide what action and what punishment will be given to traffic offenders on the highway, with the Greedy algorithm, the decisions taken will be based on data and facts. based on existing laws, so decisions made based on law and the human side cannot influence the decisions to be taken by the system based on the application of the Greedy algorithm. The research method used in this study uses literature reviews by reading many previous research journals, it will be able to add knowledge and deepen the research we are doing this time, with the literature review method, we will be able to find new problems and can be used as new research because The literature review is very helpful for our research this time. The system that is being used is using CCTV and can determine what decisions and punishments will be given to traffic offenders, through evidence based on images taken by cameras placed at red lights or corners of the capital's highway, the system this has been active effectively but with the implementation of the algorithm will increase. This research will produce a system proposal and be able to find out whether the application of the Greedy algorithm is correct and can help the current system by implementing the algorithm, so the existing system is more perfect. The main contribution of this research is that the use of the Greedy algorithm can help control the traffic system to enforce the law.

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Cost-Effective Vehicle Type Recognition in Surveillance Images With Deep Active Learning and Web Data

Cited by 39 publications

References 19 publications

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Application of Greedy Algorithm on Traffic Violation Enforcement

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