Introduction

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. However, despite recent developments in deep learning, very few techniques based on deep learning have been proposed to address the problem of detecting violence from videos.

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The videos are divided into 500 videos, and 500 are not fighting

Comparison of accuracy rate based on BoW method on the Hockey Fight dataset.

Accuracy comparison of MoWLD using KED and sparse coding method and proposed features based BoW model on the Hockey Fight dataset.

This dataset defines a total of 11 crowd motion patterns and it is composed of over 6000 video sequences with an average length of 100 frames per sequence. This documentation presents how to download and process the Crowd-11 dataset.

If you use this dataset, please cite our paper: Camille Dupont, Luis Tobias, and Bertrand Luvison. "Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.

Since this dataset is a composition of web videos and already existing datasets, we ask you to download and accept licence of each source and dataset. The construction of the Crowd-11 dataset is composed of two steps:

Step 1: Retrieve videos of interest from the web and/or pre-existing datasets Retrieve the pre-existing datasets of interest The pre-existing datasets are:

These datasets should be stored in their "existing_datasets/$SOURCE_NAME/" folder: . └── existing_datasets ├── agoraset ├── cuhk ├── hockey ├── peliculas │ ├── fights │ └── noFights ├── pets ├── shanghai ├── umn ├── violent_flow └── www

Copy the videos of interest from the datasets of interest The list of the videos of interest is in existing_datasets_urls.csv. To extract them into the VOI folder, execute: python existing_datasets_gathering.py The VOI folder should have the following structure: . └── VOI ├── agoraset ├── cuhk ├── hockey ├── peliculas ├── pets ├── shanghai ├── umn ├── violent_flow └── www

Download the videos of interest from the web The web sources are:

The list of the web urls to download is in web_urls.csv. The web_urls.csv file's structure is as follows :

We do not provide the script to download them, but many tools exist to do it (pytube, urllib, etc...). Note: a few videos have a ts_multiplier field. These video are in slow motion and the ts_multiplier is provided to speed them up (cf. SETPTS option in avconv).

The downloaded videos should be stored in their VOI/$SOURCE_NAME folder, which should now have the following structure: . └── VOI ├── agoraset ├── cuhk ├── gettyimages ├── hockey ├── peliculas ├── pets ├── pond5 ├── shanghai ├── umn ├── violent_flow ├── youtube └── www

Step 2: Processing original videos into the Crowd-11 dataset Once the VOI folder is complete, a preprocessing step is required in order to crop and trim the original videos into the Crowd-11 dataset.

The preprocessing.csv file's structure is as follows :

Installation:

You need to have avconv installed: sudo apt-get install avconv Then, you need to install several python package. A virtualeenv installation is recommended: virtualenv -p python3 py source py/bin/activate pip install sk-video

Execution (in the virtualenv): python script_formating.py

I enjoy the sport of ice hockey. My sons would like various statistics regarding their time on the ice during games. I would love to automate the process so that I do not have to start and stop hours of video. Ice hockey object detection can be quite difficult due to many issues. The images are quite “busy” with players, sticks. advertisements on the boards, referees, and nets. The puck is small and easily hidden. The player number is the only player differentiation and is not always visible. There are some companies developing tracking programs, but they require sensors and a special puck. No matter the difficulties, I start to play around with object identification from a game video. For this exploration, I will use Roboflow and walk you through my process. My expectations are limited for this ‘real-life’ video scenario. The datasets will be small and the images messy.

Here are a few use cases for this project:

1. Industrial Safety: This model can be used in the surveillance of industrial and construction sites where it's mandatory to wear safety gear like helmets and jackets. It could monitor the workspace and alert supervisors if someone is not properly equipped.

2. Sports Event Management: During sports events like motor racing or ice hockey, the model could detect whether all players are in their required safety gear. This would help in enforcing safety rules and prevent potential injuries.

3. Traffic Surveillance: The technology could be used in traffic surveillance systems, specifically for identifying riders without helmets on roads and highways. This would be beneficial in enforcing traffic regulations and promoting road safety.

4. Equipment Retail Management: In retail showrooms selling safety gear like helmets and jackets, the computer vision model can be used to monitor the things that customers try on, providing data on what items are most popular and assisting in inventory management.

5. Smart Home Security Systems: The technology could be integrated into home security systems to identify any intruders wearing masks or helmets, which could improve the effectiveness of the systems and ensure more reliable alerts.

EMG, plantar pressure and kinematic data across all individual high level and low level ice hockey players. (XLSX)

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Performance times of the acceleration phase, steady-state phase, and total sprint distance, of maximum effort 30m forward skating. Mean (SD).

Coefficient of multiple correlation (CMC) values (average with standard deviation in brackets) for five muscles recorded from the right lower limb (vastus medialis, gastrocnemius, gluteus medius, tibialis anterior and vastus lateralis), three joint angles (sagittal plane of right knee, sagittal plane of right hip and frontal plane of right hip), and plantar force from right skate.