AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection - CVPR NAS 2023

AI-powered video frame extraction tool that automatically identifies and extracts high-quality frames containing people, with intelligent pose categorization (standing/sitting/squatting), head orientation detection, and shot type classification. Features GPU acceleration, resumable processing, and extensive configuration options.

🏀 Basketball Video Analysis: Leverage automated detection and tracking of players, ball, and team assignments using advanced object tracking, zero-shot classification, and keypoint detection with YOLO models for comprehensive basketball game analysis

:snake: Tutorial on detecting video shot changes using Python and OpenCV. Part 1 covers basic threshold detection, Part 2 covers optimized threshold detection.

A fast video's shot detection program written in C++, based on libavcode and openCV etc.

Video shot transition detection

视频关键帧提取。

Video Summarization - Summarized a video lecture and converted it to a slideshow using Speech-to-text, Keyword extraction and OpenCV Shot detection.

Video shot detection in four algorithms

A simple yet effective python implementation for video shot detection of abrupt transition (based on python OpenCV)

Codes of RailTrack_Segmentation of TITS2021-Enhanced Few-Shot Learning for Intrusion Detection in Railway Video Surveillance

The repository contains a reference end-to-end pipeline for a real-time video analytics application. Realtime data is provided to an inference endpoint that executes single shot object detection. The metadata created during inference is then uploaded to a database for curation.

Code to perform shot detection and extraction on video

A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logically and chronologically related shots taken in a specific order to depict an over-arching concept or story.

The repository for IEEE TCSVT 2023 (A Video Shot Occlusion Detection Algorithm Based on the Abnormal Fluctuation of Depth Information)

Code for Single Shot Actor-Action Detection in Videos

This is a package to do shot boundary detection on videos.

Modality-Aware Shot Relating and Comparing for Video Scene Detection

shot boundary detection(video classification) by fully convolutional network

Video shot detection is a computer vision technique used to automatically identify the boundaries between shots in a video sequence.

Implementation of the Owl-ViT model for zero-shot object detection in videos.

The repository for IEEE ICASSP 2022 (A Light Weight Model for Video Shot Occlusion Detection)

Histogram Difference based Shot Boundary detection, as used in Jung et al.'s paper on recaptured video detection, 2015.

A general object detection application supporting ONNX models, image/video/camera inputs, with one-shot and real-time inference modes, and an intuitive GUI.

This project applies advanced computer vision techniques to analyze tennis gameplay, measuring player speed, ball shot speed, and shot count from video. Utilizing YOLO for player and ball detection and CNNs implemented with PyTorch for court keypoint extraction.

1.1 Project Introduction The Indian education landscape has been undergoing rapid changes for the past 10 years owing to the advancement of web-based learning services, specifically, eLearning platforms. Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users mark in 2021. Although the market is growing on a rapid scale, there are major challenges associated with digital learning when compared with brick and mortar classrooms. One of many challenges is how to ensure quality learning for students. Digital platforms might overpower physical classrooms in terms of content quality but when it comes to understanding whether students are able to grasp the content in a live class scenario is yet an open-end challenge. In a physical classroom during a lecturing teacher can see the faces and assess the emotion of the class and tune their lecture accordingly, whether he is going fast or slow. He can identify students who need special attention. Digital classrooms are conducted via video telephony software program (exZoom) where it’s not possible for medium scale class (25-50) to see all students and access the mood. Because of this drawback, students are not focusing on content due to lack of surveillance. While digital platforms have limitations in terms of physical surveillance but it comes with the power of data and machines which can work for you. It provides data in the form of video, audio, and texts which can be analysed using deep learning algorithms. Deep learning backed system not only solves the surveillance issue, but it also removes the human bias from the system, and all information is no longer in the teacher’s brain rather translated in numbers that can be analysed and tracked. 1.2 Problem Statements We will solve the above-mentioned challenge by applying deep learning algorithms to live video data. The solution to this problem is by recognizing facial emotions. 1.2.1 Face Emotion Recognition This is a few shot learning live face emotion detection system. The model should be able to real-time identify the emotions of students in a live class.

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Deep Learning-Based Approaches for Shot Transition Detection and Known-Item Search in Video

In video processing, a video can be represented with some hierarchical structure units, such as scene, shot and frame. Also, video frame is the lowest level in the hierarchical structure. The content-based video browsing and retrieval, video-content analysis use these structure units. In video retrieval, generally, video applications must first partition a given video sequence into video shots. A video shot is defined as an image or video frame sequence that presents continuous action. The frames in a video shot are captured from a single operation of one camera. The complete video sequence is generally formed by joining two or more video shots .We have used Twilio api for messaging. It is a api which provides sid, auth-token, messaging number for sending message. Libraries used:- Client, Credentials. When any motion it detected then message will be sent on users phoneWe have used Twilio api for messaging. Software requirements are:OpenCv 3:- used for reading webcam, Annaconda:- contain all the basic libraries used for detection and tracking, Os:- for importing files from disk, Numpy :- used for all the mathematical implementation. Playsound:- to read the sound file from the memory and play it during detection.