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Tasks related to human hands have long been part of the computer visioncommunity. Hands being the primary actuators for humans, convey a lot aboutactivities and intents, in addition to being an alternative form ofcommunication/interaction with other humans and machines. In this study, wefocus on training a single feedforward convolutional neural network (CNN)capable of executing many hand related tasks that may be of use in autonomousand semi-autonomous vehicles of the future. The resulting network, which werefer to as HandyNet, is capable of detecting, segmenting and localizing (in3D) driver hands inside a vehicle cabin. The network is additionally trained toidentify handheld objects that the driver may be interacting with. To meet thedata requirements to train such a network, we propose a method for cheapannotation based on chroma-keying, thereby bypassing weeks of human effortrequired to label such data. This process can generate thousands of labeledtraining samples in an efficient manner, and may be replicated in newenvironments with relative ease.

HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands