scholarly journals Inexpensive, scalable camera system for tracking rats in large spaces

2018 ◽  
Author(s):  
Rajat Saxena ◽  
Warsha Barde ◽  
Sachin S. Deshmukh
Keyword(s):  
Tracking System ◽  
Low Cost ◽  
Video Tracking ◽  
Recording System ◽  
Raspberry Pi ◽  
Camera Tracking ◽  
Camera System ◽  
Wireless Recording ◽  
Firing Characteristics ◽  
Improved Accuracy

AbstractMost studies focused on understanding the neural circuits underlying spatial navigation are restricted to small behavioral arenas (≤ 1 m2) because of the limits imposed by the cables extending from the animal to the recording system. New wireless recording systems have significantly increased the recording range. However, the size of arena is still constrained by the lack of a video tracking system capable of monitoring the animal’s movements over large areas integrated with these recording systems. We developed and benchmarked a novel, open-source, scalable multi-camera tracking system based on commercially available and low-cost hardware (Raspberry Pi computers and Raspberry Pi cameras). This Picamera system was used in combination with a wireless recording system for characterizing neural correlates of space in environments of various sizes up to 16.5 m2. Spatial rate maps generated using the Picamera system showed improved accuracy in estimating spatial firing characteristics of neurons compared to a popular commercial system, due to its better temporal accuracy. The system also showed improved accuracy in estimating head direction cell tuning as well as theta phase precession in place cells. This improved temporal accuracy is crucial for accurately aligning videos from multiple cameras in large spaces and characterizing spatially modulated cells in a large environment.

scholarly journals Inexpensive, scalable camera system for tracking rats in large spaces

2018 ◽  
Vol 120 (5) ◽  
pp. 2383-2395 ◽  
Author(s):  
Rajat Saxena ◽  
Warsha Barde ◽  
Sachin S. Deshmukh
Keyword(s):  
Tracking System ◽  
Low Cost ◽  
Neural Correlates ◽  
Large Area ◽  
Camera System ◽  
Biologically Relevant ◽  
Commercial System ◽  
Wireless Recording ◽  
Direction Tuning ◽  
Improved Accuracy

Most studies of neural correlates of spatial navigation are restricted to small arenas (≤1 m2) because of the limits imposed by the recording cables. New wireless recording systems have a larger recording range. However, these neuronal recording systems lack the ability to track animals in large area, constraining the size of the arena. We developed and benchmarked an open-source, scalable multicamera tracking system based on low-cost hardware. This “Picamera system” was used in combination with a wireless recording system for characterizing neural correlates of space in environments of sizes up to 16.5 m2. The Picamera system showed substantially better temporal accuracy than a popular commercial system. An explicit comparison of one camera from the Picamera system with a camera from the commercial system showed improved accuracy in estimating spatial firing characteristics and head direction tuning of neurons. This improved temporal accuracy is crucial for accurately aligning videos from multiple cameras in large spaces and characterizing spatially modulated cells in a large environment. NEW & NOTEWORTHY Studies of neural correlates of space are limited to biologically unrealistically small spaces by neural recording and position tracking hardware. We developed a camera system capable of tracking animals in large spaces at a high temporal accuracy. Together with the new wireless recording systems, this system facilitates the study of neural correlates of space at biologically relevant scale. This increased temporal accuracy of tracking also improves the estimates of spatiotemporal correlates of neural activity.

scholarly journals Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6659
Author(s):  
Aryuanto Soetedjo ◽  
Evy Hendriarianti
Keyword(s):  
Execution Time ◽  
Low Cost ◽  
Infrared Camera ◽  
Raspberry Pi ◽  
Camera System ◽  
Detection And Counting ◽  
Outdoor Environments ◽  
Static Images ◽  
Leaf Detection ◽  
Non Destructive

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

scholarly journals Low-cost intelligent surveillance system based on fast CNN

2021 ◽  
Vol 7 ◽  
pp. e402
Author(s):  
Zaid Saeb Sabri ◽  
Zhiyong Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Tracking System ◽  
Low Cost ◽  
Saliency Detection ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Single Board Computer ◽  
Computational Systems

Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.

The Study of Moving Images Track Recording System which Based on Mean Shift Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1365-1370
Author(s):  
Ke Fei Wang ◽  
Hong Chang Ke
Keyword(s):  
Tracking System ◽  
Mean Shift ◽  
Video Recording ◽  
Video Tracking ◽  
Sporting Events ◽  
Camera Tracking ◽  
Mean Shift Algorithm ◽  
Development Platform ◽  
Close Relationship ◽  
Monitoring Test

Intelligent tracking system based on video recording problem is a key technique in computer vision and research focus, a lot of engineering applications and video tracking technology has a close relationship. This system is a development platform for the ADSP-BF533 core, together with the VisualDSP + + integrated development and debugging environment, implementation, and optimization MeanShift tracking algorithm to achieve real-time tracking of moving target shooting can be used in sporting events, intelligent monitoring. Test data show that the system can control the PTZ camera tracking rigid and non rigid shooting targeting.

Development and validation of a low-cost mobile robotics testbed

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Michael Johnson ◽  
Martin Hayes
Keyword(s):  
Real Time ◽  
Mobile Robotics ◽  
Vision System ◽  
Tracking System ◽  
Low Cost ◽  
Research Work ◽  
Cost Effective ◽  
Video Tracking ◽  
Research And Education ◽  
Development And Validation

AbstractThis paper considers the design, construction and validation of a low-cost experimental robotic testbed, which allows for the localisation and tracking of multiple robotic agents in real time. The testbed system is suitable for research and education in a range of different mobile robotic applications, for validating theoretical as well as practical research work in the field of digital control, mobile robotics, graphical programming and video tracking systems. It provides a reconfigurable floor space for mobile robotic agents to operate within, while tracking the position of multiple agents in real-time using the overhead vision system. The overall system provides a highly cost-effective solution to the topical problem of providing students with practical robotics experience within severe budget constraints. Several problems encountered in the design and development of the mobile robotic testbed and associated tracking system, such as radial lens distortion and the selection of robot identifier templates are clearly addressed. The testbed performance is quantified and several experiments involving LEGO Mindstorm NXT and Merlin System MiaBot robots are discussed.

scholarly journals A noninterfering system to measure in-cage spontaneous physical activity in mice

2018 ◽  
Vol 125 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Chiel Poffé ◽  
Sebastiaan Dalle ◽  
Hans Kainz ◽  
Emanuele Berardi ◽  
Peter Hespel
Keyword(s):  
Physical Activity ◽  
Home Cage ◽  
Tracking System ◽  
Low Cost ◽  
Data Interpretation ◽  
Video Tracking ◽  
Movement Speed ◽  
Dark Light ◽  
Wide Range ◽  
Spontaneous Physical Activity

Due to lack of low-cost and convenient measurement procedures, uncontrolled changes in spontaneous physical activity (SPA) level often are insufficiently considered as a confounding factor in rodent studies. Nonetheless, alterations in SPA can significantly impact on a wide range of physiological measurements. Therefore, we developed an accurate, low-cost video tracking procedure to allow routine assessment of SPA in the home cage of experimental animals (i.e., mice) and in the absence of any distress that might cause alterations in SPA. SPA parameters acquired (movement distance, movement time, and movement speed) with the novel tracking system were identical to those simultaneously obtained with a high-end and well-validated movement-tracking device (mean error = 0.15 ± 0.07%, r = 0.99, P < 0.001). To further validate the setup, we also demonstrated caffeine-induced stimulation of SPA (195% more activity compared with vehicle, P < 0.01), we adequately reproduced typical SPA fluctuations inherent to day/night cycles (146 and 702% more active during nocturnal compared with diurnal cycle for Balb/c and C57BL/6J mice, respectively, P < 0.001), and we confirmed previously documented SPA differences between animal strains (24% less activity in C57BL/6J mice compared with Balb/c mice, P < 0.05). Taken together, we provide data to prove that this novel low-cost methodology can be conveniently used in any mouse experiment where uncontrolled changes in SPA due to experimental interventions might confound data interpretation. By analogy, the system can be used to document a beneficial impact of therapeutic interventions on SPA in any disease mouse model. NEW & NOTEWORTHY We developed a low-cost procedure to routinely measure SPA in mice. The procedure maintains normal SPA because the animals continue to stay in their home cage in the absence of any external manipulation by the investigators and under habitual dark/light ambient conditions. This novel methodology can be conveniently used in any mouse experiment to quantify experimentally induced alterations in SPA or to assess natural variations in SPA that might confound data interpretation.

scholarly journals Optical Tracking System

2015 ◽  
Vol 61 (2) ◽  
pp. 165-170 ◽  
Author(s):  
Gernot Korak ◽  
Gernot Kucera
Keyword(s):  
Tracking System ◽  
Low Cost ◽  
Industrial Robots ◽  
Optical Tracking ◽  
Process Chain ◽  
Optical Tracking System ◽  
Camera System ◽  
Ethernet Connection ◽  
Cost Components ◽  
Selection Of

Abstract The presented optical tracking system allows intuitive controlling and programming of industrial robots by demonstration. The system is engineered with low cost components. Using an active marker (IR-LEDs) in combination with a stereo vision configuration of the camera system and the selection of suitable algorithms for the process chain of the image processing a positioning accuracy in the range of millimeters has been achieved. The communication between the tracking system and the robot is realized by using the TCP/IP protocol via an Ethernet connection.

scholarly journals Designing of an Advanced Compression Bioreactor with an Implementation of a Low-Cost Controlling System Connected to a Mobile Application

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 915
Author(s):  
Gözde Dursun ◽  
Muhammad Umer ◽  
Bernd Markert ◽  
Marcus Stoffel
Keyword(s):  
Mobile Application ◽  
Low Cost ◽  
Experimental Information ◽  
Raspberry Pi ◽  
Tissue Constructs ◽  
Cost Controlling ◽  
Controlling System ◽  
And Control ◽  
Tissue Models

(1) Background: Bioreactors mimic the natural environment of cells and tissues by providing a controlled micro-environment. However, their design is often expensive and complex. Herein, we have introduced the development of a low-cost compression bioreactor which enables the application of different mechanical stimulation regimes to in vitro tissue models and provides the information of applied stress and strain in real-time. (2) Methods: The compression bioreactor is designed using a mini-computer called Raspberry Pi, which is programmed to apply compressive deformation at various strains and frequencies, as well as to measure the force applied to the tissue constructs. Besides this, we have developed a mobile application connected to the bioreactor software to monitor, command, and control experiments via mobile devices. (3) Results: Cell viability results indicate that the newly designed compression bioreactor supports cell cultivation in a sterile environment without any contamination. The developed bioreactor software plots the experimental data of dynamic mechanical loading in a long-term manner, as well as stores them for further data processing. Following in vitro uniaxial compression conditioning of 3D in vitro cartilage models, chondrocyte cell migration was altered positively compared to static cultures. (4) Conclusion: The developed compression bioreactor can support the in vitro tissue model cultivation and monitor the experimental information with a low-cost controlling system and via mobile application. The highly customizable mold inside the cultivation chamber is a significant approach to solve the limited customization capability of the traditional bioreactors. Most importantly, the compression bioreactor prevents operator- and system-dependent variability between experiments by enabling a dynamic culture in a large volume for multiple numbers of in vitro tissue constructs.

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