Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.
No matter how well-regarded a particular airport happens to be, the slog from curb to cabin is pretty much the same wherever you go. A decades-old paradigm of queues, security screens, snack vendors, and gate-waiting prevails--the only difference is the level of stress. Transiting a modern hub such as Munich or Seoul is more easily endured than threading your way through the perpetual construction zones that pass for airports around New York. The sky portal of the 2040s, however, is likely to be free of such delights. Many of us will be driven to the terminal by autonomous cars; our eyes, faces, and fingers will be scanned; and our bags will have a permanent ID that allows them to be whisked from our homes before we even set out.
Range anxiety, the bugaboo of all-electric driving, is even more frightening for all-electric flying, where running out of power has worse consequences than having to pull over to the side of the road. A solution now comes from Workhorse, an Ohio-based firm. It has a passenger-carrying air taxi, called the SureFly, which combines the company's expertise in partially automated operation, from its drone business, and in hybrid-electric propulsion, from its truck business. The craft's eight counter-rotating motors each drive a carbon-fiber rotor, and the power comes from a generator cranked by an internal-combustion engine. You can fly 110 kilometers (70 miles) on a tank, then refill in minutes.
The promise of hyperloop ranks near the top of the spectacular index: a network of tubes that will shoot people and their things from city to city at near supersonic speeds. But even if you never clamber into a levitating pod, the work being done now to make hyperloop a reality could make your future journeys--whether by plane, train, or automobile--faster, comfier, and cooler.