Scaling Video Analytics on Constrained Edge Nodes

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application microclassifiers that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content.

EDF: Ensemble, Distill, and Fuse for Easy Video Labeling

We present a way to rapidly bootstrap object detection on unseen videos using minimal human annotations. We accomplish this by combining two complementary sources of knowledge (one generic and the other specific) using bounding box merging and model distillation. The first (generic) knowledge source is obtained from ensembling pre-trained object detectors using a novel bounding box merging and confidence reweighting scheme. We make the observation that model distillation with data augmentation can train a specialized detector that outperforms the noisy labels it was trained on, and train a Student Network on the ensemble detections that obtains higher mAP than the ensemble itself. The second (specialized) knowledge source comes from training a detector (which we call the Supervised Labeler) on a labeled subset of the video to generate detections on the unlabeled portion. We demonstrate on two popular vehicular datasets that these techniques work to emit bounding boxes for all vehicles in the frame with higher mean average precision (mAP) than any of the reference networks used, and that the combination of ensembled and human-labeled data produces object detections that outperform either alone.

3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning

The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final model accuracy and necessitate additional ML techniques to compensate for this loss, limiting their generality. Some attempts to reduce communication incur high computation overhead, which makes their performance benefits visible only over slow networks. We present 3LC, a lossy compression scheme for state change traffic that strikes balance between multiple goals: traffic reduction, accuracy, computation overhead, and generality. It combines three new techniques---3-value quantization with sparsity multiplication, quartic encoding, and zero-run encoding---to leverage strengths of quantization and sparsification techniques and avoid their drawbacks. It achieves a data compression ratio of up to 39--107X, almost the same test accuracy of trained models, and high compression speed. Distributed ML frameworks can employ 3LC without modifications to existing ML algorithms. Our experiments show that 3LC reduces wall-clock training time of ResNet-110--based image classifiers for CIFAR-10 on a 10-GPU cluster by up to 16--23X compared to TensorFlow's baseline design.