Sound source localization for video surveillance camera

Stachurski, Jacek; Netsch, Lorin; Cole, Randy

doi:10.1109/avss.2013.6636622

Cited by 16 publications

(4 citation statements)

References 9 publications

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“…In this work, we deal with two problems, sound classification and localization, which are the tasks of identifying the type of sound and estimating the location of sound sources. For decades, both tasks have been studied as a crucial component for various domains such as robotics [9], [10], speech recognition [11], and surveillance system [12]. Generally, sound localization is more challenging than sound classification since the number of sources, the interaction between sources and surroundings, and the movement of sources should be considered [13] in the case of localization.…”

Section: Related Workmentioning

confidence: 99%

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Park,

Cho,

Sim

et al. 2022

Preprint

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Recently, deep learning-based methods have drawn huge attention due to their simple yet high performance without domain knowledge in sound classification and localization tasks. However, a lack of gun sounds in existing datasets has been a major obstacle to implementing a support system to spot criminals from their gunshots by leveraging deep learning models. Since the occurrence of gunshot is rare and unpredictable, it is impractical to collect gun sounds in the real world. As an alternative, gun sounds can be obtained from an FPS game that is designed to mimic real-world warfare. The recent FPS game offers a realistic environment where we can safely collect gunshot data while simulating even dangerous situations. By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks. The BGG dataset consists of 37 different types of firearms, distances, and directions between the sound source and a receiver. We carefully verify that the in-game gunshot data has sufficient information to identify the location and type of gunshots by training several sound classification and localization baselines on the BGG dataset. Afterward, we demonstrate that the accuracy of real-world firearm classification and localization tasks can be enhanced by utilizing the BGG dataset.

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Section: Related Workmentioning

confidence: 99%

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Park,

Cho,

Sim

et al. 2022

Preprint

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“…Video surveillance with automated video detection is still the most important sensor modality for security surveillance in public areas [1]. Audio detection is also used, especially for the detection and localization of certain strong acoustic events such as firearms or braking glass [2,3]. However, even a small false positive rate per sensor from automatic detection leads to a significant number of false alarms as the number of sensors increases.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor fusion for the security surveillance of public areas

Litzenberger,

Hubner,

Kohn

et al. 2023

International Conference on Images, Signals, and Computing (ICISC 2023)

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Increasing security awareness in the public sector are leading to a more and more widespread use of surveillance applications. Although the available technologies like video processing are already well advanced, they still suffer from high false alarm rates when used under realistic conditions. We present a method for sensor fusion based on probability density maps and a rule engine. The system was tested in a public area using the combination of audio localization, audio classification and video detection using 79 simulated scenarios and 44 hours of sample data recorded over a period of several weeks. The false positive rate decreased by 60% and the event localization rate increased by 25% with the fusion approach compared to the detection performance of individual techniques.

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“…Current solutions that monitor the activity inside the vehicle are mostly video-based systems [11][12][13]. In other fields, these video-based solutions tend to be very useful as they can look through facial or object movements to find possible sound sources in the environment [14][15][16][17]. However, videoonly solutions are not able to capture all the surroundings as they have a limited field of view, making the classification and detection of all human actions inside the vehicle difficult.…”

Section: Introductionmentioning

confidence: 99%

Microphone Array for Speaker Localization and Identification in Shared Autonomous Vehicles

et al. 2022

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With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, active monitoring of all passengers is required. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in the hardware the sound localization algorithms.

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Sound source localization for video surveillance camera

Cited by 16 publications

References 9 publications

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

Multi-sensor fusion for the security surveillance of public areas

Microphone Array for Speaker Localization and Identification in Shared Autonomous Vehicles

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