BioSense: Real-Time Object Tracking for Animal Movement and Behavior Research

Patman, Jon; Michael, Sabrina C. J.; Lutnesky, Marvin M. F.; Palaniappan, Kannappan

doi:10.1109/aipr.2018.8707411

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…The third category, which includes this work, involves real-time videos captured using ground-level cameras. An important application of such methods is in ethology for which many general purpose end-to-end graphical interface systems have been developed [71,92,108,86,76]. Methods specialized for particular species like cows [123], beef cattle [99] and tigers [62] have also been proposed where the latter includes re-identification that is typically done using camera trap images.…”

Section: Related Workmentioning

confidence: 99%

Animal Detection in Man-made Environments

Singh

Pietrasik

Natha

et al. 2020

2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Automatic detection of animals that have strayed into human inhabited areas has important security and road safety applications. This paper attempts to solve this problem using deep learning techniques from a variety of computer vision fields including object detection, tracking, segmentation and edge detection. Several interesting insights are elicited into transfer learning while adapting models trained on benchmark datasets for real world deployment. Empirical evidence is presented to demonstrate the inability of detectors to generalize from training images of animals in their natural habitats to deployment scenarios of man-made environments. A solution is also proposed using semi-automated synthetic data generation for domain specific training. Code and data used in the experiments are made available to facilitate further work in this domain.

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Section: Related Workmentioning

confidence: 99%

Animal Detection in Man-made Environments

Singh

Pietrasik

Natha

et al. 2020

2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…Patman et al [12] developed a technology called Biosense to track animal movement. Their main sensor is a camera, and they use video image processing to track animal movement and behavior.…”

Section: Image Processing Machine Learning and Uav Wildlife Trackingmentioning

confidence: 99%

“…Let us analyze Algorithm 2 in depth. for ble_active_triggering_interval = ble active triggering interval in [10,12,15,20,25,30,35,40,45,50] if mod(t, ble_active_period_interval) == 0 then…”

Section: Simulator Algorithmsmentioning

confidence: 99%

Building an Energy-Efficient Ad-Hoc Network for Wildlife Observation

Camal

Akşanlı

2020

Electronics

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This study evaluated the design of an energy-efficient ad-hoc network used for wildlife observations, particularly in order to understand the social relationships in an animal group, where the distance between individuals, i.e., proximity, can be used to measure a relationship. Our proposed network consists of a full mesh topology and contains nodes that communicate via Bluetooth Low Energy (BLE) in advertisement mode. The initial hardware configuration and software algorithm duty cycles the BLE communication to on and off states using a parameter called the BLE active triggering interval. The algorithm is improved by placing the BLE subsystem and CPU in deep sleep when there are no BLE or CPU tasks to process. This improves the power performance by up to 94.48%. To scale up power optimization and track the trade-off between power and throughput, we created a simulator that modeled our network with dynamic wireless sensor nodes. The simulator verified the base case hardware results. It also showed a median power performance increase of 97.79% in comparison to the base case, yet throughput decreased by 66.65%. The highest power performance increased by 98.89% when a wireless sensor node was configured with a BLE active triggering interval of 50 s and its CPU was set to 14 MHz; however, the simulator showed a throughput drop of 79.97%. Depending on the application, a design may tolerate the decline in throughput to achieve higher power performance.

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“…A recent review of the currently available open-source tools for animal behavioral video analysis is presented in [ 6 ]. Another important type of the proposed organism motion monitoring applications uses Kalman filter-based methods [ 7 , 8 , 9 , 10 ]. Other articles present animal activity tracking by means of AI (deep learning) or the optical flow method [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…‘Bio-Sense’, described in [ 8 ], is a computing program that ensures object detection and then employs an adaptive Kalman filter to model the motion dynamics of different animals.…”

Section: Introductionmentioning

confidence: 99%

Captive Animal Behavior Study by Video Analysis

Rotaru,

Bejinariu,

Costin

et al. 2023

Sensors

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Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat’s head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis.

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BioSense: Real-Time Object Tracking for Animal Movement and Behavior Research

Cited by 13 publications

References 14 publications

Animal Detection in Man-made Environments

Animal Detection in Man-made Environments

Building an Energy-Efficient Ad-Hoc Network for Wildlife Observation

Captive Animal Behavior Study by Video Analysis

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