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a-dataset-for-automatic-violence-detection-in-videos

action-tubes-for-violence-detection

actiontubes

source code for Finding Action Tubes, CVPR 2015

activity_rec_ml-lstm

Activity Recognition using Temporal Optical Flow Convolutional Features and Multi-Layer LSTM

anet2016-cuhk

Action Recognition Toolbox for CUHK&ETHZ&SIAT submission to ActivityNet 2016

anomaly-detection-in-surveillance-videos

Real-World Anomaly Detection in Surveillance Videos

avss2019

Efficient Violence Detection Using 3D Convolutional Neural Networks

awesome-violence-detection

Collections for Violence Detection

bat-image-classification

This is an official implementation of our CVPR 2020 paper "Non-Local Neural Networks With Grouped Bilinear Attentional Transforms".

bert

TensorFlow code and pre-trained models for BERT

biconvlstm-for-violence-detection-in-videos

BiConvLSTM for violence detection in videos

biconvlstm_violence_detection

This Pytorch repo uses BiConvLSTM in a Spatiotemporal Encoder to detect violence in Videos. Three benchmark datasets namely Hockey, Movies and Violent Flows were used in this work.

bidirectionallstm

Action Recognition in Video Sequences using Deep Bi-directional LSTM with CNN Features

c3d

C3D is a modified version of BVLC caffe to support 3D ConvNets.

cnn-bilstm-model

convolutional-long-short-term-memory-based-iot-node-for-violence-detection

Abstract— Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model’s sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of ConvLSTM model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized convolutional neural networks (CNNs) to learn spatial features from video’s frames that were applied to Long Short- Term Memory (LSTM) for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as playing football, basketball, tennis, swimming and eating. The experimental results show high performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %.

data-efficient-video-transformer

menovideo: pytorch library for video action recognition and video understanding

detecting-violence

Implementation of the model ( Violence Detection) using CNN+ LSTM and tensorflow and keras as backend )

detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

dynamic-image-nets

Dynamic Image Networks for Action Recognition

fight-detection-surv-dataset

New generated dataset for fight detection in surveillance cameras.

fight_detection_from_surveillance_cameras-pytorch_project

Fight Detection From Surveillance Cameras by fine-tuning a PyTorch Pretrained Model

hr-mscnn

a HR-based multi-stream CNN descriptor (HR-MSCNN) is formulated to recognize human action

human-self-learning-anomaly

Code for the paper "Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events", IJCB 2020

human-violence-detection

A human violence detection & classification system using recurrent neural networks(RNN).

kinetics-i3d

Convolutional neural network model for video classification trained on the Kinetics dataset.

liteflownet-for-video

mmaction2

OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

online-action-detection

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022.

online-realtime-action-recognition-based-on-openpose

A skeleton-based real-time online action recognition project, classifying and recognizing base on framewise joints, which can be used for safety surveilence.