Violence detection opencv

Since edge detection is susceptible to noise in the image, first step is to remove the noise in the image with a 5x5 Gaussian filter.

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We have already seen this in previous chapters. From these two images, we can find edge gradient and direction for each pixel as follows:. Gradient direction is always perpendicular to edges. It is rounded to one of four angles representing vertical, horizontal and two diagonal directions. After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted pixels which may not constitute the edge.

For this, at every pixel, pixel is checked if it is a local maximum in its neighborhood in the direction of gradient.

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Check the image below:. Point A is on the edge in vertical direction. Gradient direction is normal to the edge. Point B and C are in gradient directions. So point A is checked with point B and C to see if it forms a local maximum.

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If so, it is considered for next stage, otherwise, it is suppressed put to zero. This stage decides which are all edges are really edges and which are not. For this, we need two threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to be edges and those below minVal are sure to be non-edges, so discarded.

Liveness Detection with OpenCV

Those who lie between these two thresholds are classified edges or non-edges based on their connectivity. If they are connected to "sure-edge" pixels, they are considered to be part of edges. Otherwise, they are also discarded. See the image below:.Deep Learning Face Applications Tutorials. In this tutorial, you will learn how to perform liveness detection with OpenCV. You will create a liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems.

However, a common question I get asked over email and in the comments sections of the face recognition posts is:. Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system.

violence detection opencv

Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smartphone that they could hold up to the camera responsible for performing face recognition such as in the image at the top of this post. How could you apply anti-face spoofing algorithms into your facial recognition applications? To learn how to incorporate liveness detection with OpenCV into your own face recognition systems, just keep reading!

Face recognition systems are becoming more prevalent than ever. Face recognition systems can be circumvented simply by holding up a photo of a person whether printed, on a smartphone, etc.

To keep our example straightforward, the liveness detector we are building in this blog post will focus on distinguishing real faces versus spoofed faces on a screen. This algorithm can easily be extended to other types of spoofed facesincluding print outs, high-resolution prints, etc. You can use these videos as a starting point for your dataset but I would recommend gathering more data to help make your liveness detector more robust and accurate. With testing, I determined that the model is slightly biased towards my own face which makes sense because that is all the model was trained on.

In the rest of the tutorial, you will learn how to take the dataset I recorded it and turn it into an actual liveness detector with OpenCV and deep learning. In order of appearance in this tutorial, the three scripts are:. Lines import our required packages. We also initialize two variables for the number of frames read as well as the number of frames saved while our loop executes Lines 31 and In order to perform face detection, we need to create a blob from the image Lines 53 and Our script makes the assumption that there is only one face in each frame of the video Lines This helps prevent false positives.Deep Learning Object Detection Tutorials.

R-CNNs are one of the first deep learning-based object detectors and are an example of a two-stage detector. The problem with the standard R-CNN method was that it was painfully slow and not a complete end-to-end object detector. Girshick et al. The Fast R-CNN algorithm made considerable improvements to the original R-CNN, namely increasing accuracy and reducing the time it took to perform a forward pass; however, the model still relied on an external region proposal algorithm.

These algorithms treat object detection as a regression problem, taking a given input image and simultaneously learning bounding box coordinates and corresponding class label probabilities. In general, single-stage detectors tend to be less accurate than two-stage detectors but are significantly faster. First introduced in by Redmon et al. Redmon and Farhadi are able to achieve such a large number of object detections by performing joint training for both object detection and classification.

YOLOv3 is significantly larger than previous models but is, in my opinion, the best one yet out of the YOLO family of object detectors. But seriously, if you do nothing else today read the YOLOv3 tech report. Open up the yolo. All you need installed for this script OpenCV 3.

For the time being I recommend going for OpenCV 3. You can actually be up and running in less than 5 minutes with pip as well. First, we import our required packages — as long as OpenCV and NumPy are installed, your interpreter will breeze past these lines. Command line arguments are processed at runtime and allow us to change the inputs to our script from the terminal. Our command line arguments include:.

violence-detection

What good is object detection unless we visualize our results? Applying non-maxima suppression suppresses significantly overlapping bounding boxes, keeping only the most confident ones. Previously it failed for some inputs and resulted in an error message. Finally, we display our resulting image until the user presses any key on their keyboard ensuring the window opened by OpenCV is selected and focused.

Here you can see that YOLO has not only detected each person in the input image, but also the suitcases as well! While both the wine bottle, dining table, and vase are correctly detected by YOLO, only one of the two wine glasses is properly detected.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This work is based on violence detection model proposed by [1] with minor modications. The original model was implemented with Pytorch [2] while in this work we implement it with Keras and TensorFlow as a back-end. The model takes as an inputs the raw video, converts it into frames and output a binary classication of violence or non-violence label. Skip to content.

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How to do Object Detection with OpenCV [LIVE]

Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. IEEE, You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Jul 28, Jul 15, Feb 8, Jul 13, Add a description, image, and links to the violence-detection topic page so that developers can more easily learn about it.

violence detection opencv

Curate this topic. To associate your repository with the violence-detection topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 7 public repositories matching this topic Language: All Filter by language. Star Code Issues Pull requests. Surveillance System Against Violence. Star 3. Updated Feb 6, Jupyter Notebook. Star 2. Updated Jun 10, Python. Updated Jan 22, Python. Star 0. Updated Feb 11, Python. Updated Mar 12, Jupyter Notebook.

Star 1. Updated Mar 9, Python. Improve this page Add a description, image, and links to the violence-detection topic page so that developers can more easily learn about it.

Add this topic to your repo To associate your repository with the violence-detection topic, visit your repo's landing page and select "manage topics.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.It is very important to automatically detect violent behaviors in video surveillance scenarios, for instance, railway stations, gymnasiums and psychiatric centers.

However, the previous detection methods usually extract descriptors around the spatiotemporal interesting points or extract statistic features in the motion regions, leading to limited abilities to effectively detect video-based violence activities. To address this issue, we propose a novel method to detect violence sequences. Firstly, the motion regions are segmented according to the distribution of optical flow fields.

Secondly, in the motion regions, we propose to extract two kinds of low-level features to represent the appearance and dynamics for violent behaviors. Thirdly, the extracted features are coded using Bag of Words BoW model to eliminate redundant information and a specific-length vector is obtained for each video clip. Experimental results on three challenging benchmark datasets demonstrate that the proposed detection approach is superior to the previous methods.

In public places, violent behaviors pose a serious threat to personal security and social stability.

violence detection opencv

At present, millions of equipment are applied in public places, leading to a huge pressure on the security attendants. Therefore, it is of great significance to automatically detect violence events from the vast amounts of surveillance video data. For the consideration of different applications including video annotation, video retrieving and real-time monitoring, we focus on the challenging task of detecting violent activities in surveillance videos.

This task involves many related computer vision techniques, for instance, object detection, action recognition and classification. Referring to the definition provided by Schedi et al.

The goal of violence detection is to automatically and effectively determine whether the violence occurs or not within a short video sequence. In the field of video-based violence detection, it is difficult to capture effective and discriminative features as a result of the variations of human body.

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The variations are mainly caused by scale, view point, mutual occlusion and dynamic scenes. In early attempts, most researches detected violence scenes by recognizing some violence-related characteristics like flame, blood, gunshots, explosions and car-braking [ 2 — 4 ]. However, this kind of method is limited by its disadvantages, such as low detection rate and high false alarm. Besides, these characteristics are not suitable for the general surveillance systems which always lack the audio information.

In recent studies about violence detection, some spatiotemporal descriptors around the interest points have received great popularity, such as STIPs [ 56 ] and MoSIFT [ 7 — 9 ].

To recognize the human actions in surveillance videos, Chen and Hauptmann [ 7 ] designed MoSIFT descriptor, which not only encoded the local appearance but also explicitly modeled local motion. Then, a bigram model was applied to capture the co-occurrence of two video words. Xu et al. To eliminate redundant features and obtain more discriminative features, the non-parametric Kernel Density Estimation KDE and sparse coding were exploited to select the MoSIFT descriptors and process the selected features.

Then, the typical BoW model was used before classification. Senst et al. In Reference [ 15 ], a novel approach, that could effectively describe dynamic characteristics in violent videos, was reported for violence detection. By integrating the direction-based Lagrangian field measure into the SIFT descriptor, a new feature for violence analysis was developed.It is very important to automatically detect violent behaviors in video surveillance scenarios, for instance, railway stations, gymnasiums and psychiatric centers.

However, the previous detection methods usually extract descriptors around the spatiotemporal interesting points or extract statistic features in the motion regions, leading to limited abilities to effectively detect video-based violence activities.

To address this issue, we propose a novel method to detect violence sequences. Firstly, the motion regions are segmented according to the distribution of optical flow fields. Secondly, in the motion regions, we propose to extract two kinds of low-level features to represent the appearance and dynamics for violent behaviors.

Thirdly, the extracted features are coded using Bag of Words BoW model to eliminate redundant information and a specific-length vector is obtained for each video clip. Experimental results on three challenging benchmark datasets demonstrate that the proposed detection approach is superior to the previous methods. This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author s received no specific funding for this work. Space Star Technology Company Limited did not provide any support in the form of salary for author QD and did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Qinghai Ding. There are no patents, products in development or marketed products to declare involving this manuscript.

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In public places, violent behaviors pose a serious threat to personal security and social stability. At present, millions of equipment are applied in public places, leading to a huge pressure on the security attendants. Therefore, it is of great significance to automatically detect violence events from the vast amounts of surveillance video data. For the consideration of different applications including video annotation, video retrieving and real-time monitoring, we focus on the challenging task of detecting violent activities in surveillance videos.

YOLO object detection with OpenCV

This task involves many related computer vision techniques, for instance, object detection, action recognition and classification. Referring to the definition provided by Schedi et al. The goal of violence detection is to automatically and effectively determine whether the violence occurs or not within a short video sequence. In the field of video-based violence detection, it is difficult to capture effective and discriminative features as a result of the variations of human body.

The variations are mainly caused by scale, view point, mutual occlusion and dynamic scenes. In early attempts, most researches detected violence scenes by recognizing some violence-related characteristics like flame, blood, gunshots, explosions and car-braking [ 2 — 4 ]. However, this kind of method is limited by its disadvantages, such as low detection rate and high false alarm.

Besides, these characteristics are not suitable for the general surveillance systems which always lack the audio information.

In recent studies about violence detection, some spatiotemporal descriptors around the interest points have received great popularity, such as STIPs [ 56 ] and MoSIFT [ 7 — 9 ]. To recognize the human actions in surveillance videos, Chen and Hauptmann [ 7 ] designed MoSIFT descriptor, which not only encoded the local appearance but also explicitly modeled local motion.

Then, a bigram model was applied to capture the co-occurrence of two video words. Xu et al. To eliminate redundant features and obtain more discriminative features, the non-parametric Kernel Density Estimation KDE and sparse coding were exploited to select the MoSIFT descriptors and process the selected features.


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