Today, the amount of public violence has increased dramatically. As much in high schools as in the street. This has resulted in the ubiquitous use of surveillance cameras. This has helped the authorities to identify these events and take the necessary measures. But almost all systems today require the human-inspection of these videos to identify such events, which is virtually inefficient. It is therefore necessary to have such a practical system that can automatically monitor and identify the surveillance videos. The development of various deep learning techniques, thanks to the availability of large data sets and computational resources, has resulted in a historic change in the community of computer vision. Various techniques have been developed to address problems such as object detection, recognition, tracking, action recognition, legend generation, etc.
Fig: Decoder Network Architecture
data path are defined as follows:
- Clone this repository and change directory to this repository
git clone https://github.com/manncodes/Violence-Detection-CNNLSTM.git
cd Violence-Detection-CNNLSTM- Download Inbuilt Keras VGG16 model by running the following script:
python download_imagenet.py-
Now download the pretrained model H5 file from here and put it inside the model folder so it'll be "model/vlstm_92.h5".
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Install Dependencies
pip install -r requirements.txt- to run inference on a video existing in 'data' folder run following command:
python infer.py <Video name existing in data folder>
# Example
python infer.py fi3_xvid.avi- to run inference live:
python infer_cam.pyExample of 2 consecutive inference of a violent video.
to be noted better version of live inference is made by us in a different repository. Do contact me to get access to it. That repository also contains an android app for the violence detection UI.
Results on Hocket Dataset after hypertuning of parameters
