Learning to Detect Violent Videos using Convolution LSTM

This work is based on a violence detection model proposed by [1] with minor modifications. The original model was implemented with Pytorch [2]. in this work, we implement it with Keras and TensorFlow as a back-end. The model incorporates a pre-trained convolution Neural Network (CNN) connected to Convolutional LSTM (ConvLSTM) layer. The model takes as an input the raw video, converts it into frames, and output a binary classification of violence or non-violence label.

Architecture structure

Running configurations

Video datasets paths:

data path are defined as follows:

• hocky - data/raw_videos/HockeyFights - Data_Link
• violentflow - data/raw_videos/violentflow - Data_Link
• movies - data/raw_videos/movies - Data_Link

Libraries perquisites:

• python 2.7
• numpy 1.14.0
• keras 2.2.0
• tensorflow 1.9.0
• Pillow 3.1.2
• opencv-python 3.4.1.15

Running operation:

just run python run.py (currently we don't support arguments from command line)

Results

Hyper-tuning results (Hocky data)

Hockey dataset results

Refrences

1. Sudhakaran, Swathikiran, and Oswald Lanz. "Learning to detect violent videos using convolution long short-term memory." In Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on, pp. 1-6. IEEE, 2017.
2. https://github.com/swathikirans/violence-recognition-pytorch