# Focus: Querying Large Video Datasets with Low Latency and Low Cost

arXiv: https://arxiv.org/abs/1801.03493 · HTML: https://mlsanity.com/paper/1801.03493/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 30 of 46)

- [Coverage-centric Coreset Selection for High Pruning Rates](https://mlsanity.com/paper/2210.15809/) (2022)
- [GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge](https://mlsanity.com/paper/2201.07705/) (2022)
- [Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks](https://mlsanity.com/paper/2109.14320/) (2021)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving](https://mlsanity.com/paper/2312.05385/) (2023)
- [Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics](https://mlsanity.com/paper/2106.15315/) (2021)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale](https://mlsanity.com/paper/2105.06524/) (2021)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Accelerating Deep Learning Inference via Learned Caches](https://mlsanity.com/paper/2101.07344/) (2021)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [On Efficient Approximate Queries over Machine Learning Models](https://mlsanity.com/paper/2206.02845/) (2022)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Extract-Transform-Load for Video Streams](https://mlsanity.com/paper/2310.04830/) (2023)
- [CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics](https://mlsanity.com/paper/2207.00588/) (2022)
- [MOSEL: Inference Serving Using Dynamic Modality Selection](https://mlsanity.com/paper/2310.18481/) (2023)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference](https://mlsanity.com/paper/2604.06036/) (2026)
- [Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning](https://mlsanity.com/paper/2107.03964/) (2021)
- [Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding](https://mlsanity.com/paper/2512.07344/) (2025)
- [Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs](https://mlsanity.com/paper/2206.04687/) (2022)
- [Towards Automatic Model Specialization for Edge Video Analytics](https://mlsanity.com/paper/2104.06826/) (2021)
- [ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration](https://mlsanity.com/paper/2308.10068/) (2023)
- [Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural Networks](https://mlsanity.com/paper/2107.01354/) (2021)
- [Subgraph Stationary Hardware-Software Inference Co-Design](https://mlsanity.com/paper/2306.17266/) (2023)
- [EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices](https://mlsanity.com/paper/2308.08717/) (2023)
- [VQPy: An Object-Oriented Approach to Modern Video Analytics](https://mlsanity.com/paper/2311.01623/) (2023)
- [BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge](https://mlsanity.com/paper/2312.15740/) (2023)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [VID-WIN: Fast Video Event Matching with Query-Aware Windowing at the Edge for the Internet of Multimedia Things](https://mlsanity.com/paper/2105.02957/) (2021)

## Abstract (arXiv)

Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering "after the fact" queries: identify video frames with objects of certain classes (cars, bags) from many days of recorded video. While advancements in convolutional neural networks (CNNs) have enabled answering such queries with high accuracy, they are too expensive and slow. We build Focus, a system for low-latency and low-cost querying on large video datasets. Focus uses cheap ingestion techniques to index the videos by the objects occurring in them. At ingest-time, it uses compression and video-specific specialization of CNNs. Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time. To reduce query time latency, we cluster similar objects and hence avoid redundant processing. Using experiments on video streams from traffic, surveillance and news channels, we see that Focus uses 58X fewer GPU cycles than running expensive ingest processors and is 37X faster than processing all the video at query time.