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Abnormal event detection is a growing demand to process a plethora of surveillance videos. Our project helps detect the abnormality in videos with high accuracy, thus saving time for organizations and individuals who would have to go through the entire footage instead.
The project an OpenCV based C++ implementation of the paper 'Abnormal Event Detection at 150FPS in Matlab'
Codes for "Abnormal Event Detection in Videos using Spatiotemporal Autoencoder".
Abnormal Event Detection in Videos using SpatioTemporal AutoEncoder
Code examples for the book, Creating GUI Application with wxPython
Script to extract CNN deep features with different ConvNets, and then use them for an Image Classification task with a SVM classifier with lineal kernel over the following small datasets: Soccer [1], Birds [2], 17flowers [3], ImageNet-6Weapons[4] and ImageNet-7Arthropods[4].
Circle detection by arc-support line segments
This is a training project to learn how to train convolutional networks to recognise objects in images.
This repository contains an OpenCV program for classifying the color of an object in images with a Support Vector Machine (SVM)
This contains all assignments(codes and corresponding recepies) for COMP140, Computational Thinking, which is an introduction to Python and some algorithms. In this class, we used a online python editor, codeskulptor developed by our professors, Scott Rixner.
deep_learning_for_computer_vision 电子学习代码
Full tutorial of computer vision and machine learning basics with OpenCV and Keras in Python.
Computer Vision with Python 3, published by Packt
Matlab codes for concrete crack detection using TuFF
『ゼロから作る Deep Learning』のリポジトリ
主要记录Deep Learning For Computer Vision With Python的代码
Dictionary Learning and Sparse Coding for unsupervised clustering
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
The Event Annotation Tool is an instrument for annotating normal and abnormal events in videos/images.
:octocat: Find pearls on open-source seashore 分享 GitHub 上有趣、入门级的开源项目
Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.
This repository contains scripts for Human Activity Recognition (HAR) project
Human Activity Recognition
A small school project which is about correctly classifying main objects in images, using a provided dataset.
Lane detection and tracking
Matlab implementation of the paper "Learning fast approximations of sparse coding"
A small web app that allow users to create account, login, and make a bucket list.
用python爬取数据,存入MySQL,然后用Django开发小说网站
Detect objects in image and classify them into classes to recognize each object class
Project developed in python to develop an object detection system using OpenCV software. The main functionalities displayed in this project include Object Detection based on color that is to classify objects in images according to colour , Pedestrian detection , Human face detection, Vehicle motion Detection from a video file which can be used to detect traffic in a particular area.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.