Introduction

As a full-stack developer, I'm always excited to explore new technologies and features that can enhance user experience. Recently, I came across Gemini's ability to natively embed videos, which sparked an idea to build a sub-second video search feature. I wanted to create a system that could quickly search and retrieve specific moments from a large collection of videos.

The Challenge

The biggest challenge I faced was to achieve sub-second search results. To accomplish this, I needed to design an efficient indexing system that could quickly locate specific frames within a video. I decided to use a combination of computer vision and machine learning techniques to analyze the video content and create a searchable index.

Video Indexing

To create the index, I used the following steps:

• Video Preprocessing: I used OpenCV to preprocess the videos and extract frames at regular intervals.
• Frame Analysis: I used a convolutional neural network (CNN) to analyze each frame and extract features such as objects, scenes, and actions.
• Indexing: I stored the extracted features in a database, along with the corresponding frame numbers and video IDs.

Search Algorithm

To search for specific moments in the videos, I implemented a search algorithm that uses the indexed features to quickly locate the relevant frames. The algorithm works as follows:

1. Query Analysis: The user inputs a search query, which is analyzed using natural language processing (NLP) techniques to extract keywords and intent.
2. Index Querying: The extracted keywords are used to query the database and retrieve a list of relevant frame numbers and video IDs.
3. Frame Retrieval: The relevant frames are retrieved from the video files using the frame numbers and video IDs.

Implementation

I implemented the video search feature using Node.js, React, and TypeScript. I used the following libraries and tools:

• ssrajadh/sentrysearch: I used this library as a reference to implement the video indexing and search algorithm.
• OpenCV: I used OpenCV for video preprocessing and frame analysis.
• TensorFlow.js: I used TensorFlow.js to implement the CNN for frame analysis.

1import * as cv from 'opencv4nodejs';
2import * as tf from '@tensorflow/tfjs';
3
4// Video preprocessing and frame analysis
5const video = new cv.VideoCapture('video.mp4');
6const frames = [];
7while (video.read()) {
8  const frame = video.getFrame();
9  frames.push(frame);
10}
11const analyzer = tf.sequential();
12analyzer.add(tf.layers.conv2d({
13  inputShape: [224, 224, 3],
14  filters: 32,
15  kernelSize: 3,
16  activation: 'relu'
17}));
18// ...
19```

Conclusion

In this post, I shared my experience of building a sub-second video search feature using Gemini's native video embedding capability. I implemented an efficient indexing system using computer vision and machine learning techniques, and a search algorithm that quickly locates specific moments in a large collection of videos. The implementation was done using Node.js, React, and TypeScript, and I used libraries such as OpenCV and TensorFlow.js to analyze the video content.