2020 Challenge Tracks

Detailed participant instructions can be accessed here.

Participants can compete in one or more of the following four challenges:

Challenge Track 1: Vehicle Counts by Class at Multiple Intersections

Participating teams will count four-wheel vehicles and freight trucks that follow pre-defined movements from multiple camera scenes. For example, teams will perform vehicle counting separately for left-turning, right-turning and through traffic at a given intersection approach. This helps traffic engineers understand the traffic demand and freight ratio on individual corridors, which can be used to design better intersection signal timing plans and apply other traffic congestion mitigation strategies when necessary. To maximize the practical value of the outcome from this track, both the vehicle counting effectiveness and the program execution efficiency will contribute to the final score for each participating team. The team with the highest score will be declared the winner of this track.

Challenge Track 2: City-Scale Multi-Camera Vehicle Re-Identification

Participating teams will perform vehicle re-identification based on vehicle crops from multiple cameras placed at multiple intersections. This helps traffic engineers understand journey times along entire corridors. In this year’s challenge, the training set will be composed of both real-world data and synthetic data. The usage of synthetic data is encouraged as it can be simulated under various environments and can produce large training data sets. The team with the highest accuracy in detecting vehicles that appear in multiple cameras will be declared the winner of this track. In the event, that multiple teams perform equally well in this track, the algorithm needing the least amount of manual supervision will be chosen as the winner.

Challenge Track 3: City-Scale Multi-Camera Vehicle Tracking

Participating teams will track vehicles across multiple cameras both at a single intersection and across multiple intersections spread out across a city. This helps traffic engineers understand journey times along entire corridors. The team with the highest accuracy in detecting vehicles that appear in multiple cameras will be declared the winner of this track.In the event that multiple teams perform equally well in this track, the algorithm needing the least amount of manual supervision will be chosen as the winner.

Challenge Track 4: Traffic Anomaly Detection

Participating teams will submit at most 100 anomalies detected, including wrong turns, wrong driving direction, lane change errors, and all other anomalies, based on video feeds available from multiple cameras at intersections and along highways. The team with the highest average precision in anomaly detection in the submitted anomalies will be announced the winner of this track.