In today’s digital age, videos have become a ubiquitous form of media, with thousands of hours of video being generated and consumed every minute. However, with such a vast amount of video data available, it can be challenging for users to find the specific content they are seeking. This is where video summarization techniques can play a vital role.

Video summarization is the process of condensing a video into a shorter and more concise representation. It aims to capture the most important and relevant information from the original video, allowing users to quickly grasp the content without having to watch the entire video.

In this blog post, we will explore some video summarization techniques using C++. We will discuss two popular approaches: keyframe extraction and keyshot selection.

Keyframe Extraction

Keyframe extraction is one of the fundamental video summarization techniques. It involves selecting a small subset of frames, called keyframes, that best represent the content of the video. These keyframes are then used to summarize the video.

Here is an example code snippet in C++ that demonstrates keyframe extraction using the OpenCV library:

#include <opencv2/opencv.hpp>
#include <vector>

std::vector<cv::Mat> extractKeyframes(cv::VideoCapture video, int numKeyframes) {
    std::vector<cv::Mat> keyframes;
    int totalFrames = video.get(cv::CAP_PROP_FRAME_COUNT);
    int stepSize = totalFrames / numKeyframes;

    cv::Mat frame;
    int frameIndex = 0;
    while (video.read(frame)) {
        if (frameIndex % stepSize == 0) {
            keyframes.push_back(frame);
        }
        frameIndex++;
    }

    return keyframes;
}

int main() {
    cv::VideoCapture video("input.mp4");
    std::vector<cv::Mat> keyframes = extractKeyframes(video, 10);

    for (const auto& frame : keyframes) {
        cv::imshow("Keyframe", frame);
        cv::waitKey();
    }

    return 0;
}

In this example, we use the OpenCV library to read the video frames and extract keyframes at regular intervals based on the desired number of keyframes. The extractKeyframes function takes a VideoCapture object and the number of desired keyframes as input, and returns a vector of keyframe images.

Keyshot Selection

Keyshot selection is another popular video summarization technique that goes beyond keyframes. It aims to identify representative shots from the video, which may contain multiple frames. This approach takes into account the temporal coherence between frames to select shots that capture the essence of the video.

Here is an example code snippet in C++ that demonstrates keyshot selection using the FFmpeg library:

#include <iostream>
#include <vector>
extern "C" {
#include <libavformat/avformat.h>
}

std::vector<int> selectKeyshots(const std::string& videoFilename, int numKeyshots) {
    av_register_all();
    AVFormatContext* formatContext = nullptr;

    if (avformat_open_input(&formatContext, videoFilename.c_str(), nullptr, nullptr) != 0) {
        std::cerr << "Failed to open video file" << std::endl;
        return {};
    }

    if (avformat_find_stream_info(formatContext, nullptr) < 0) {
        std::cerr << "Failed to find video stream information" << std::endl;
        avformat_close_input(&formatContext);
        return {};
    }

    int videoStreamIndex = -1;
    for (unsigned int i = 0; i < formatContext->nb_streams; i++) {
        if (formatContext->streams[i]->codecpar->codec_type == AVMEDIA_TYPE_VIDEO) {
            videoStreamIndex = i;
            break;
        }
    }

    if (videoStreamIndex == -1) {
        std::cerr << "No video stream found" << std::endl;
        avformat_close_input(&formatContext);
        return {};
    }

    std::vector<int> keyshots;
    int totalFrames = formatContext->streams[videoStreamIndex]->nb_frames;
    int stepSize = totalFrames / numKeyshots;

    for (int i = 0; i < numKeyshots; i++) {
        int frameIndex = i * stepSize;
        keyshots.push_back(frameIndex);
    }

    avformat_close_input(&formatContext);

    return keyshots;
}

int main() {
    std::vector<int> keyshots = selectKeyshots("input.mp4", 5);

    for (const auto& frameIndex : keyshots) {
        std::cout << "Keyshot: " << frameIndex << std::endl;
    }

    return 0;
}

This code snippet demonstrates keyshot selection using the FFmpeg library. The selectKeyshots function takes the filename of the video and the number of desired keyshots as input, and returns a vector of frame indices corresponding to the selected keyshots.

These are just two examples of video summarization techniques using C++. There are many other advanced techniques and algorithms available. Video summarization is a fascinating field that combines computer vision, machine learning, and multimedia processing to make video content more accessible and manageable.

Conclusion

Video summarization techniques are essential for efficient video browsing and content retrieval. Keyframe extraction and keyshot selection are two widely used approaches that help condense videos into shorter and more meaningful representations. By implementing these techniques using C++, developers can build powerful video summarization applications and systems.

Whether you are working on video indexing, video search, or simply trying to navigate through large video collections, video summarization techniques can significantly enhance the user experience.