Francesc Guim

Daniel Rivas, Francesc Guim, Jordà Polo et al.

As the number of installed cameras grows, so do the compute resources required to process and analyze all the images captured by these cameras. Video analytics enables new use cases, such as smart cities or autonomous driving. At the same time, it urges service providers to install additional compute resources to cope with the demand while the strict latency requirements push compute towards the end of the network, forming a geographically distributed and heterogeneous set of compute locations, shared and resource-constrained. Such landscape (shared and distributed locations) forces us to design new techniques that can optimize and distribute work among all available locations and, ideally, make compute requirements grow sublinearly with respect to the number of cameras installed. In this paper, we present FoMO (Focus on Moving Objects). This method effectively optimizes multi-camera deployments by preprocessing images for scenes, filtering the empty regions out, and composing regions of interest from multiple cameras into a single image that serves as input for a pre-trained object detection model. Results show that overall system performance can be increased by 8x while accuracy improves 40% as a by-product of the methodology, all using an off-the-shelf pre-trained model with no additional training or fine-tuning.

Daniel Rivas, Francesc Guim, Jordà Polo et al.

Judging by popular and generic computer vision challenges, such as the ImageNet or PASCAL VOC, neural networks have proven to be exceptionally accurate in recognition tasks. However, state-of-the-art accuracy often comes at a high computational price, requiring hardware acceleration to achieve real-time performance, while use cases, such as smart cities, require images from fixed cameras to be analyzed in real-time. Due to the amount of network bandwidth these streams would generate, we cannot rely on offloading compute to a centralized cloud. Thus, a distributed edge cloud is expected to process images locally. However, the edge is, by nature, resource-constrained, which puts a limit on the computational complexity that can execute. Yet, there is a need for a meeting point between the edge and accurate real-time video analytics. Specializing lightweight models on a per-camera basis may help but it quickly becomes unfeasible as the number of cameras grows unless the process is automated. In this paper, we present and evaluate COVA (Contextually Optimized Video Analytics), a framework to assist in the automatic specialization of models for video analytics in edge cameras. COVA automatically improves the accuracy of lightweight models through their specialization. Moreover, we discuss and review each step involved in the process to understand the different trade-offs that each one entails. Additionally, we show how the sole assumption of static cameras allows us to make a series of considerations that greatly simplify the scope of the problem. Finally, experiments show that state-of-the-art models, i.e., able to generalize to unseen environments, can be effectively used as teachers to tailor smaller networks to a specific context, boosting accuracy at a constant computational cost. Results show that our COVA can automatically improve accuracy of pre-trained models by an average of 21%.