MonkeyTrail:A scalable video-based method for tracking macaque movement trajectory in daily living cages

Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.

Radarsat observations and forecasting of oil slick trajectory movements

RADARSAT data have a potential role for coastal pollution monitoring. This study presents a new approach to detect and forecast oil slick trajectory movements. The oil slick trajectory movements is based on the tidal current effects and Fay's algorithm for oil slick spreading mechanisms. The oil spill trajectory model contains the integration between Doppler frequency shift model and Lagrangian model. Doppler frequency shift model implemented to simulate tidal current pattern from RADARSAT data while the Lagrangian model used to predict oil spill spreading pattern. The classical Fay's algorithm was implemented with the two models to simulate the oil spill trajectory movements.The study shows that the slick lengths are effected by tidal current V component with maximum velocity of 1.4 m/s. This indicates that oil slick trajectory path is moved towards the north direction. The oil slick parcels are accumulated along the coastline after 48 h. The analysis indicated that tidal current V components were the dominant forcing for oil slick spreading.

Movement Primitives as a Robotic Tool to Interpret Trajectories Through Learning-by-doing

Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.

Development of unilateral obstacle-avoiding mower for Y-trellis pear orchard

To solve the problem of weeding under Y-trellis pear orchards,a unilateral obstacle-avoiding mower(UOAM)was developed in this study.The mower is composed of an obstacle-avoiding disc mechanism,a hydraulic profiling mechanism,and a cutting disc mechanism.The diameter of the obstacle-avoiding disc was 0.7 m,which could swing around the trunk or ground pile actuated by the spring mechanism.The piston movement of the hydraulic cylinder controls the working position of the obstacle-avoiding disc.The maximum extension distance of the hydraulic profiling arm was 1 m.Based on the national standards and actual situation of orchards,the optimum parameters were determined with a combination of advancing speed of 0.44 m/s,rotation speed of the cutting disc at 2000 r/min,blade number of 2,and cutting edge length of 0.2 m.Finally,the design parameters were verified by the mathematical model of the blade cutting edge trajectory and multi-body dynamics simulation.Taking stubble cutting stability,leakage rate,working efficiency and costs as the test indexes,field performance comparison tests were carried out on the three operation modes of UOAM mowing,shoulder carrying mower(SCM)mowing and manual weeding.Test results showed that the coefficient variation of stubble cutting height of UOAM was the smallest,showing that the working stability of UOAM was better than the other two treatments.The leakage rate of UOAM was 2.42%,and its coefficient variation was lower than that of SCM.The working efficiency of UOAM was much higher than that of SCM and manual weeding,which was 4.44 times of SCM and 20 times of manual weeding.The profitable area of UOAM was 7.02 hm^(2),showing that it was suitable for large-scale mechanized Y-trellis pear orchards.