TraClets: Harnessing the power of computer vision for trajectory classification
Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information and generate insights and meaningful information. To this end, researchers have developed several trajectory classification algorithms over the years that are able to annotate tracking data. Similarly, in this research, a novel methodology is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way, through computer vision techniques. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms. Experimental results demonstrate that TraClets achieves a classification performance that is comparable to, or in most cases, better than the state-of-the-art, acting as a universal, high-accuracy approach for trajectory classification.
Code (1)
Tasks
ClassificationSimilar Papers 제목 키워드 기반
Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs
Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow interaction with human experts, limiting their…
SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory
Accurately predicting the destination of taxi trajectories can have various benefits for intelligent location-based services. One potential method to accomplish this prediction is by converting the taxi trajectory into a…
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this …
Feature EngineeringStructured PredictionFocusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE
Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation. Recent advancements have focused on adaptively allocating computational resources across diver…
Vision HGNN: An Image is More than a Graph of Nodes
The realm of graph-based modeling has proven its adaptability across diverse real-world data types. However, its applicability to general computer vision tasks had been limited until the introduction of the Vision Gr…
graph constructionGraph Neural Networkimage-classificationImage Classification+2