publications

2025

1. SIGMOD

   SketchQL: Video Moment Querying with a Visual Query Interface

   Renzhi Wu*, Pramod Chunduri*, Ali Payani, Xu Chu, Joy Arulraj, and Kexin Rong

   SIGMOD, 2025

2023

1. Preprint

   Tracer: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing

   Pramod Chunduri, Yao Lu, and Joy Arulraj

   Preprint, 2023
2. DEEM

   EVA: An End-to-End Exploratory Video Analytics System

   Gaurav Tarlok Kakkar, Jiashen Cao, Pramod Chunduri, Zhuangdi Xu, Suryatej Reddy Vyalla, Prashanth Dintyala, and 5 more authors

   In DEEM Workshop at SIGMOD, 2023
3. VLDB

   Seiden: Revisiting Query Processing in Video Database Systems

   Jaeho Bang, Gaurav Tarlok Kakkar, Pramod Chunduri, Subrata Mitra, and Joy Arulraj

   VLDB, 2023

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   State-of-the-art video database management systems (VDBMSs) often use lightweight proxy models to accelerate object retrieval and aggregate queries. The key assumption underlying these systems is that the proxy model is an order of magnitude faster than the heavyweight oracle model. However, recent advances in computer vision have invalidated this assumption. Inference time of recently proposed oracle models is on par with or even lower than the proxy models used in state-of-the-art (SoTA) VDBMSs. This paper presents Seiden, a VDBMS that leverages this radical shift in the runtime gap between the oracle and proxy models. Instead of relying on a proxy model, Seiden directly applies the oracle model over a subset of frames to build a query-agnostic index, and samples additional frames to answer the query using an exploration-exploitation scheme during query processing. By leveraging the temporal continuity of the video and the output of the oracle model on the sampled frames, Seiden delivers faster query processing and better query accuracy than SoTA VDBMSs. Our empirical evaluation shows that Seiden is on average 6.6 x faster than SoTA VDBMSs across diverse queries and datasets.

2022

1. SIGMOD

   Zeus: Efficiently localizing actions in videos using reinforcement learning

   Pramod Chunduri, Jaeho Bang, Yao Lu, and Joy Arulraj

   In SIGMOD, 2022

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   Detection and localization of actions in videos is an important problem in practice. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries because actions often involve a complex interaction between objects and are spread across a sequence of frames; detecting and localizing them requires computationally expensive deep neural networks. It is also important to consider the entire sequence of frames to answer the query effectively. In this paper, we present ZEUS, a video analytics system tailored for answering action queries. We present a novel technique for efficiently answering these queries using deep reinforcement learning. ZEUS trains a reinforcement learning agent that learns to adaptively modify the input video segments that are subsequently sent to an action classification network. The agent alters the input segments along three dimensions - sampling rate, segment length, and resolution. To meet the user-specified accuracy target, ZEUS’s query optimizer trains the agent based on an accuracy-aware, aggregate reward function. Evaluation on three diverse video datasets shows that ZEUS outperforms state-of-the-art frame- and window-based filtering techniques by up to 22.1x and 4.7x, respectively. It also consistently meets the user-specified accuracy target across all queries.