T-800 Vision by the Terminator Jonathan Russo, Asaf Shamir, Baruch Segal

Intro The T-800 app allows everyone to see the world through Terminator’s Infra-Red vision eyes. Recognition The app’s primary engine focuses on the recognition of all living creatures in sight, using advanced methods of face recognition. Each creature is then tracked tirelessly over time to ensure its precise assessment.

Classification Each of the recognized creatures is classified – innocent or threat. The classification is performed by identifying a unique marker in close proximity of the creature. T-800 is able to detect two different markers:  Colored polygons in general and specifically red triangles  Target like markers consisting of two different color circles

Threat Elimination To allow making the world a safer place, the app is able to eliminate threats. The elimination of threats is done by a powerful “laser” stripping the enemy to its bare skeleton. User Interface Color vision (off) Color vision (on) Threat Elimination Color Adjustment Marker (Triangle) Marker (Target) White Balancing Color Picker

Challenge: Color Vision The main challenge in displaying a live feed from the camera after a color palette transformation is performance. Some improvements were introduced in order to face this particular challenge, but the main optimization was achieved by transforming each channel separately.

Challenge: Tracking Face detection is far from perfect, and tracking moving objects can be even more challenging. However, the accumulated data gathered from multiple frames holds an opportunity as well. A reputation algorithm that follows detected locations over time assists avoiding false detections.

Challenge: Marker Identification In order to classify threats, a unique marker is identified in the proximity of a recognized creature. While matching identified markers to creatures in their vicinity is a fairly simple task, identifying the markers in the first place is quite complicated and holds two main challenges:

 Color – alternating light can cause color to appear very different. To tackle this challenge a color picker and a white balancing method were introduced.  Shape – point of view, as well as lighting, can make shapes look rugged. The use of approximation algorithms helps identify polygons better, but optimal results were obtained using a new target marker.