Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Vision and sensor-based modeling of intelligent on-demand fish feeding machine

Currently, handfeeding is the most accurate method for fish feed distribution, as the responses of the fish can be directly observed. However, it is labor-intensive, costly, and needs skilled laborers. The time-based feeding is inaccurate as it does not reflect the actual feeding responses. In its essence, the technological advances for intelligent precision fish feeding still need to be developed. For instance, the on-demand fish feeders using sensors are prone to false hunger detection. Furthermore, the existing behavior-based feeding method using computer vision (computer vision) has several challenges, including real-time operation, costly hardware requirements, and low accuracy in turbid water conditions. In this study, an on-demand intelligent fish-feeding machine was developed to optimize autonomous operation for feeding management systems in recirculating aquaculture systems. A fusion of emerging technologies was utilized to minimize the drawbacks of each technology, namely computational intelligence, computer vision, sensors, and the Internet of Things (IoT). Specifically, a fuzzy inference system was used for the decision support system (DSS) to autonomously feed the fish using the accelerometer sensor as the direct triggering mechanism to activate the feed dispenser. In contrast, fish feeding behavior and excess feeds were quantified using computer vision and deep learning, which served as the second feedback control to eliminate the feeding decision errors from the sensor. Other input parameters were considered in the DSS, such as the total dispensed feeds and the time duration of the latest dispensed feeds. The measures of the latter two input parameters were made possible with Edge computing and IoT. Moreover, an IoT-based water quality monitoring system was also developed to ensure that the water is within the ideal condition, specifically for tilapia. With the developed system, significant contributions are noted in the context of optimizing fish feeding management: (1) implementation of DSS for monitoring and control in a remote and real-time manner with an IoT visualization dashboard, (2) intelligent and autonomous operation of the fish-feeding machine, and (3) technical advances and improvement of fish feeding behavior recognition. In the context of the gathered data from the monitoring system, further insights can be leveraged for prediction, diagnosis, and anomaly detection relating to feeding management

Speech activation for Internet of things security system in public utility vehicles and Taxicabs

Public transport vehicles are widely preferred by the mass because of the accessibility it provides. Due to its easy access, crimes like robbery, assaults and even homicides are experienced by the drivers. Hence, vehicle tracking, and alert systems are built to improve its security and safety. The existing systems are limited to physical triggering which offers minimal effectiveness because the buttons may unintentionally be pressed, or the driver is hesitant to move and unable to press the button when necessary. To eliminate the inconvenience caused by a physically triggered security system, a non-contact activation was developed with the use of speech recognition, and the Internet of Things (IoT). This study presents the evaluation of the transcription confidence level associated with background noises using the Google Speech Recognition API and the implementation of the security system in IoT. The results show that speech recognition has acquired 100% transcription accuracy around 50 dBA to 78 dBA background noise using the native language, while the tested operation latency is approximately 43 seconds during the deployment. The study paved a way for a convenient noncontact triggering security system to elevate the rapid response of crime-related incidents in public vehicle drivers through immediate notification and provision of the necessary information to authorities. © 2019 IEEE