438 Open source projects that are alternatives of or similar to Anomaly Detection In Surveillance Videos

Go package for computer vision using OpenCV 4 and beyond.

A quick and dirty vehicle speed detector using video + anomaly detection

A Computation Graph Virtual Machine Based Deep Learning Framework

crnn.caffe

This repo is implemented based on detectron2 and centernet

Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

A Deep Learning Framework customized for Sunway TaihuLight

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (ICCV 2017)

📹 一个短视频APP视频内容安全审核的思路调研及实现汇总

Dockerfile and runscripts for FlowNet 2.0 (estimation of optical flow)

Sherlock is an anomaly detection service built on top of Druid

Minimal runtime core of Caffe, Forward only, GPU support and Memory efficiency.

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

A deep convolutional neural network system for live emotion detection

Modified RefineDet

ImageNet pre-trained models with batch normalization for the Caffe framework

Multi-layer Recurrent Neural Networks (with LSTM) for character-level language models in Caffe

MobileNet-v2 experimental network description for caffe

IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet

arcface and retinaface model convert mxnet to onnx.

Self Maintained Caffe. In this version Faster-RCNN, RFCN needs layer are fully supported!

Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

Caffe for Sparse and Low-rank Deep Neural Networks

No description or website provided.

Deep Learning Benchmarking Suite

XNOR-Net, CUDNN5 supported version of XNOR-Net-caffe: https://github.com/loswensiana/BWN-XNOR-caffe

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…

An Unsupervised Graph-based Toolbox for Fraud Detection

Implemetation of Pyramid Scene Parsing Network in Pytorch

Anomaly detection implemented in Keras

A Caffe Implementation of SRGAN

Implementation of Visual Feature Attribution using Wasserstein GANs (VAGANs, https://arxiv.org/abs/1711.08998) in PyTorch

The SimNets Architecture's Implementation in Caffe

Tutorial for computer vision and machine learning in PHP 7/8 by opencv (installation + examples + documentation)

Semantic Image Segmentation by Scale-Adaptive Networks (TIP 2019)

This repository stores the files used for my summer internship's work on "teacher-student learning", an experimental method for training deep neural networks using a trained teacher model.

TOD: GPU-accelerated Outlier Detection via Tensor Operations

The data is the future of oil, digging the potential value of the data is very meaningful. This library records my road of machine learning study.

Code for reproducing f-AnoGAN in Pytorch

Log-based Impactful Problem Identification using Machine Learning [FSE'18]

Convert image folder to lmdb, adapted from Efficient-PyTorch

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

The Kubervisor allow you to control which pods should receive traffic or not based on anomaly detection.It is a new kind of health check system.

This is an implementation of very deep two stream CNNs for action recognition. The implementation is inspired by Wang et. al., https://github.com/yjxiong/caffe. Some improvements from Wang's implementation include reading videos from LDMB database, faster testing using LDMB interface. The aim is to work better with big dataset such as UCF101, HMDB51, Sports1M and ActivityNet easily.

some anomaly detection models and experiments.

[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

Main component extraction for outlier detection

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Implementation for <Large-Margin Softmax Loss for Convolutional Neural Networks> in ICML'16.

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.

List of tools & datasets for anomaly detection on time-series data.

log anomaly detection toolkit including DeepLog

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)

MobilNet-SSD object detection in opencv 3.4.1

An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks

Deploying a Batch Scoring Pipeline for Python Models