Video-surveillance system for remote long-term in situ observations: recording diel cavity use and behaviour of wild European lobsters (Homarus gammarus)

2014 ◽  
Vol 65 (12) ◽  
pp. 1094 ◽  
Author(s):  
Ronny Steen ◽  
Sondre Ski

Long-term studies of subtidal marine animals in the wild are a demanding enterprise. Traditionally, data collection has been limited to direct observations during SCUBA diving. In the past decade, video technology has improved rapidly and behavioural monitoring of marine organisms has successfully been conducted by using modern video-recording equipment. Here, we describe a video-monitoring system that employs video motion detection (VMD) and describe its use with the European lobster (Homarus gammarus). There is a shortage of detailed information on lobster behaviour in the wild, with virtually no published data on the fine-scale behaviour of the European lobster under natural conditions. This dearth of information reflects the difficulties in observing behaviour in nocturnal marine animals. Here, we explore whether a remote video-surveillance system is suitable for long-term monitoring of European lobsters inhabiting an artificial cavity in a natural habitat. From the video recordings, we were able to register diel cavity use and categorise behavioural elements such as resting, feeding, burrowing and substrate moving, self-cleaning, burrow occupancy and interactions among individuals. We propose that this novel system will contribute to more efficient data sampling of lobsters and facilitate non-invasive, long-term behavioural studies of other marine and freshwater animals.

2015 ◽  
Vol 66 (4) ◽  
pp. 385
Author(s):  
Ronny Steen ◽  
Sondre Ski

Long-term studies of subtidal marine animals in the wild are a demanding enterprise. Traditionally, data collection has been limited to direct observations during SCUBA diving. In the past decade, video technology has improved rapidly and behavioural monitoring of marine organisms has successfully been conducted by using modern video-recording equipment. Here, we describe a video-monitoring system that employs video motion detection (VMD) and describe its use with the European lobster (Homarus gammarus). There is a shortage of detailed information on lobster behaviour in the wild, with virtually no published data on the fine-scale behaviour of the European lobster under natural conditions. This dearth of information reflects the difficulties in observing behaviour in nocturnal marine animals. Here, we explore whether a remote video-surveillance system is suitable for long-term monitoring of European lobsters inhabiting an artificial cavity in a natural habitat. From the video recordings, we were able to register diel cavity use and categorise behavioural elements such as resting, feeding, burrowing and substrate moving, self-cleaning, burrow occupancy and interactions among individuals. We propose that this novel system will contribute to more efficient data sampling of lobsters and facilitate non-invasive, long-term behavioural studies of other marine and freshwater animals.


2014 ◽  
Vol 19 (2-3) ◽  
pp. 51-58
Author(s):  
Jaromir Przybylo ◽  
Joanna Grabska-Chrzastowska ◽  
Przemyslaw Korohoda

Abstract Automated and intelligent video processing and analysis systems are becoming increasingly popular in video surveillance. Such systems must meet a number of requirements, such as threat detection and real-time video recording. Furthermore, they cannot be expensive and must not consume too much energy because they have to operate continuously. The work presented here focuses on building a home video surveillance system matching the household budget and possibly making use of hardware available in the house. Also, it must provide basic functionality (such as video recording and detecting threats) all the time, and allow for a more in-depth analysis when more computing power be available.


2007 ◽  
Vol 33 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Panagiotis Dendrinos ◽  
Eleni Tounta ◽  
Alexandros A. Karamanlidis ◽  
Anastasios Legakis ◽  
Spyros Kotomatas

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.


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