sceneQuery: Semantically Search through Videos

Inspiration

Ever been in a situation where you desperately wanted to find that one scene from your favorite movie or TV show but couldn't remember where it was? Or needed to find specific moments in hours-long security camera footage? sceneQuery is a tool designed for precisely these scenarios. It lets you semantically search through any video with just a text prompt.

What it does

• Semantic Search: sceneQuery lets users input textual descriptions or prompts and find scenes or clips in videos that match the description.
• Support for Various Formats: Whether it's movies, TV shows, long videos, or security camera recordings, sceneQuery works across formats.
• Quick Preview: Once a match is found, users can preview the scene to confirm it's what they were looking for.
• Time-Stamp Navigation: Each result comes with a timestamp, allowing for quick navigation to the exact moment in the source video. ## How we built it
• Machine Learning Backbone: At the core of sceneQuery is a sophisticated deep learning model trained on a diverse dataset of videos. This model understands both the visual content and any accompanying audio.
• Natural Language Processing (NLP): To understand and interpret user queries, we employed state-of-the-art NLP techniques. This ensures that the tool captures the essence of what the user is looking for.
• Indexing and Storage: Videos are pre-processed and indexed, making the search process lightning fast. This involves creating a condensed representation of video content without compromising on the quality of search results. ## Challenges we ran into
• Handling Diverse Data: The diverse nature of video content meant we had to ensure our model was robust across various scenes, lighting conditions, and audio qualities.
• Storage and Indexing: With vast amounts of video data, creating an efficient storage and indexing mechanism was challenging.
• Speed and Efficiency: Ensuring real-time results for users meant optimizing our algorithms for both speed and accuracy. ## Accomplishments that we're proud of
• High Accuracy Rates: sceneQuery boasts a high accuracy rate, often pinpointing the exact scene a user is thinking of.
• Scalability: Despite the complexities involved, we built sceneQuery to handle large datasets, making it suitable for both personal and enterprise-level applications.
• Intuitive User Interface: The tool is not just powerful but also user-friendly, making it accessible to even non-tech-savvy individuals. ## What we learned
• Balancing Quality and Efficiency: Building a tool like sceneQuery taught us the importance of striking a balance between providing high-quality results and ensuring efficiency.
• Importance of Diverse Training Data: The quality of search results is only as good as the data it's trained on. Ensuring diversity in our training data was crucial. ## What's next for sceneQuery
• Expanding Database: We're looking to continually expand our database to include more video content from different sources.
• Integration with Streaming Platforms: We plan on collaborating with major streaming platforms to integrate sceneQuery, making it easier for viewers to find their favorite scenes.
• Advanced Features: Future updates will include features like voice-based queries and automatic video summarization. sceneQuery is not just a tool; it's a revolution in video content navigation, making the vast world of visual media more accessible and navigable for everyone.

Built With

• c++
• clip
• css
• flask
• javascript
• python
• react
• yolo