Motorcycle’s Trajectory Tracking Model Based on Polynomial Least-squares Approximation

This paper provides a simple method for modeling the trajectory of motorcycle movements in mixed traffic condition. The trajectories model has been built using Polynomial Least-squares Approximation based on the coordinates that earned from the motorcycle maneuvers. In order to collect the data, this research uses video cameras fixed at elevated positions to record traffic flow. There are three steps of modeling motorcycle's trajectory: 1) data preprocessing to obtained pixel coordinate which represents motorcycle's trajectory, 2) coordinates transformation to convert the pixel coordinate to real-world coordinate, and 3) build a trajectory model using polynomial least-square approximation. This method was implemented for tracking the trajectory of motorcycle's maneuver. The maneuvers are when they try to avoid the collision with another motorcycle, car, and pedestrian. Based on experimental and analysis result for twenty samples of data, using a comparison of MSE for each degree of the polynomial, the trajectory model of motorcycle maneuver for avoiding an object in front of them can be described properly by 4th order polynomial equation. This result shows that trajectory model using polynomial least-squares approximation give an accurate outcome and also has the simpler computation. Furthermore, the conclusion of this study about trajectory tracking model for motorcycle may be instructive as a prior knowledge for building mixed traffic model based on their trajectory behaviors. This model also provides the information necessary for safe behavior decision making or motion planning

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available