Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Last update: Dec 16, 2021

Overview

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Description

This dataset contains the six different classes as described in our paper[]. These classes are designed to be used to detect anomalies in physical videos and are meant to be used as a benchmark to assess a method's performance on physical videos. The train set contains normal videos, the test set contains both normal and anomalous videos.

In addition, we provide a script to obtain the test and train video id's of the Something-Something V2 dataset. This too is meant to serve as even more difficult benchmark for general video anomaly detection. Our approach is decribed in the paper.

Citation

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Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Related tags

Overview

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Description

Citation

You might also like...

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

Create animations for the optimization trajectory of neural nets

Code + pre-trained models for the paper Keeping Your Eye on the Ball Trajectory Attention in Video Transformers

Learning trajectory representations using self-supervision and programmatic supervision.

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution

This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)

Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Owner

Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

SE3 Pose Interp - Interpolate camera pose or trajectory in SE3, pose interpolation, trajectory interpolation

Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

Phy-Q: A Benchmark for Physical Reasoning

Code repo for "RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network" (Machine Learning and the Physical Sciences workshop in NeurIPS 2021).