Mobile video analytics systems are often deployed in diverse environments, where domain shifts place higher demands on the responsiveness of adaptation for ”expert models” running on resource-constrained mobile devices. However, due to the lack of effective automated on-device model retrieval strategies and corresponding mobile adaptation mechanisms, most existing frameworks still rely on cloud-centric adaptation architectures rather than mobile-centric ones, leading to delayed responses to such domain shifts. We introduce a hierarchical mobile-cloud collaborative adaptation framework, AutoMOCHA, for continuous mobile video analytics in dynamically evolving environments. It includes three main contributions: (1) It facilitates efficient history expert model retrieval, without relying on dataset-based model validation, through an automatically constructed model taxonomy and indexing mechanism that does not require prior domain knowledge; (2) It proactively coordinates onboard lightweight model reuse and finetuning strategy with remote model retrieval and retraining to achieve responsive model adaptation with low latency; (3) It accelerates onboard model reuse and finetuning through designing a dedicated mobile model cache management strategy and sparse updating policy. Extensive evaluations on real-world video data across three DNN tasks demonstrate that AutoMOCHA improves model accuracy during adaptation by up to 3.4%, increases the post-clustering silhouette coefficient by up to 33%, and reduces retraining frequency by up to 10%, all while maintaining optimal response latency and retraining time.

This paper optimizes the computation efficiency for DNN-based video analytics on interconnected edge devices like surveillance cameras and unmanned aerial vehicles (UAVs). Existing solutions depend heavily on computation offloading to edge servers but overlook the potential for cross-device collaboration and leave resources on some edge devices underutilized. We instead propose AdaCollab, an adaptive multi-device collaboration framework for resource-efficient distributed edge video analytics. On the one hand, as the machine perception complexity fluctuates with runtime video content, AdaCollab dynamically calibrates DNN inspection configurations on each device without degrading the model accuracy. On the other hand, AdaCollab aligns mismatched resources and workload among edge devices by selectively offloading partial computations from overloaded devices to underloaded devices, optimizing both computation and bandwidth resource utilization. Through extensive evaluations with large-scale real-world surveillance videos on testbeds of heterogeneous NVIDIA Jetson platforms, AdaCollab outperforms the SOTA baselines by up to 25.8% in frame processing through-put and 19.8% in DNN accuracy, while exhibiting enhanced robustness under restricted network bandwidth.

Mobile video analysis systems often encounter various deploying environments, where environment shifts present greater demands for responsiveness in adaptations of deployed "expert DNN models". Existing model adaptation frameworks primarily operate in a cloud-centric way, exhibiting degraded performance during adaptation and delayed reactions to environment shifts. Instead, this paper proposes MOCHA, a novel framework optimizing the responsiveness of continuous model adaptation through hierarchical collaborations between mobile and cloud resources. Specifically, MOCHA (1) reduces adaptation response delays by performing on-device model reuse and fast fine-tuning before requesting cloud model retrieval and end-to-end retraining; (2) accelerates history expert model retrieval by organizing them into a structured taxonomy utilizing domain semantics analyzed by a cloud foundation model as indices; (3) enables efficient local model reuse by maintaining onboard expert model caches for frequent scenes, which proactively prefetch model weights from the cloud model database. Extensive evaluations with real-world videos on three DNN tasks show MOCHA improves the model accuracy during adaptation by up to 6.8% while saving the response delay and retraining time by up to 35.5× and 3.0× respectively.