Demos and Videos

Visual Target Tracking with a parallel visual processor

Quadrotor tracking visual target using SCAMP. The SCAMP returns just the bounding box (x,y,height,width) of the target location in the image plane. The onboard controller fuses this information with the IMU and height data to calculate the distance to the target.

Conference Video "Tracking control of a UAV with a parallel visual processor"

Visual Odometry with a Pixel Processor Array

Details and link to paper to follow

Feature Extraction


This work demonstrates high-speed low-power feature extraction implemented on a portable vision system based on the SCAMP-5 vision chip. This embedded system executes a parallelized FAST16 corner detection algorithm on the vision chip’s programmable on-focal-plane processor array to detect the corner points in the current image frame. The coordinates of these corner points are then extracted using the vision chip’s event address readout infrastructure. These coordinates, as sparse data, can then be output through any of the IO buses within the micro-controller in the vision system at low latency. The USB-powered (400mA) system is capable of outputting 250 features at 2300 frames per second (FPS) in ideal lighting conditions, while 1000 FPS can be achieved in an indoor environment. The system can be applied to the real-time control of agile robots and miniature aerial vehicles.

High Dynamic Range

Varying light levels across the image can make it difficult to see detail throughout the scene. SCAMP is capable of producing an HDR image prior to processing other vision algorithms to help deal with difficult lighting conditions.

The two images on the left show two extremes, first where the bright areas are properly exposed, the second where the darker areas are exposed.

Edge Detection

Visual image captured by SCAMP

Edge detection returned by SCAMP

Info about edge detection to follow

Image Scaling and Rotation

Details to follow

Tracking at 100,000 Frames Per Second

The SCAMP chip has been exploited to conduct real-time image processing operations at 100,000fps, locating a closed-shape object from amongst clutter. Further detail on this work is presented here: