Adversarially Learned Abnormal Trajectory Classifier
We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual detection threshold. Inspired by the generative adversarial network (GAN) framework, our GAN version is a discriminative one in which the discriminator is trained to distinguish normal and abnormal trajectory reconstruction errors given by a deep autoencoder. With urban traffic videos and their associated trajectories, our proposed method gives the best accuracy for abnormal trajectory detection. In addition, our model can easily be generalized for abnormal trajectory-based event detection and can still yield the best behavioural detection results as demonstrated on the CAVIAR dataset.
Code (1)
Tasks
Event DetectionGenerative Adversarial NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by …
Feature EngineeringGeneral ClassificationRobust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that models must learn effective rep-resentat…
Anomaly DetectionDenoisingNovelty DetectionSemi-supervised Anomaly Detection+1Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection
A classical approach to abnormal activity detection is to learn a representation for normal activities from the training data and then use this learned representation to detect abnormal activities while testing. Typicall…
Action DetectionActivity DetectionAnomaly DetectionPrediction+2Regression Constraint for an Explainable Cervical Cancer Classifier
This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public dataset called Herlev dataset, which co…
ClassificationGeneral ClassificationregressionPerturbation Learning Based Anomaly Detection
This paper presents a simple yet effective method for anomaly detection. The main idea is to learn small perturbations to perturb normal data and learn a classifier to classify the normal data and the perturbed data into…
Anomaly Detection