Live video analytics have become a key technology to support surveillance, security, traffic control, and even consumer multimedia applications in real time. The continuous growth in number of networked video cameras will further increase their widespread adoption. Yet, until now, developments in video analytics have largely focused on using fixed cameras, omitting the ever-growing presence of mobile cameras such as car dash-cams, drones, and smartphones. Edge computing, coupled with centralized clouds, has helped alleviate the network traffic and processing load, reducing latency and data transmissions. However, the current approach of processing video feeds through a hierarchy of clusters across a somewhat predictable path in the network will not be sufficient to support the integration of mobile feeds into the video analytics architecture. In this paper, we argue that a crucial step towards supporting heterogeneous camera sources is the adoption of a flat edge computing architecture. Such architecture should enable the dynamic distribution of processing loads through distributed computing points of presence, rapidly adapting to sudden changes in traffic conditions. In support of this hypothesis, we present exploratory results that show that smartly distributing and processing vision modules in parallel across available edge compute nodes can ultimately lead to better resource utilization and improved performance. CCS CONCEPTS • Computer systems organization → Distributed architectures; Pipeline computing; • Computing methodologies → Computer vision problems; Object detection.