Sample Yoda Benchmark Videos

Pruning Strategies

Temporal Pruning drops frames to reduce inter-frame redundancies using at least two strategies.

• Uniform frame selection
• Triggered-base frame selection

Spatial Pruning: reencodes video to reduce redundancies among pixels.

• Image quality downsizing
• Region cropping

Model Pruning: leverages the fact that videos often have specific object classes/scenes (e.g., traffic videos contain mostly vehicles/pedestrians with static background), and trims the full DNN to reduce compute cost while still achieving high accuracy.

• Model selection
• Model specialization

Object Level Features

Object Speed: the reciprocal of IoU between the bounding boxes of the same object detected in two consecutive frames, to measure how fast it moves.

Percentage of frames with objects: percentage of frames containing objects.

Per-object area: the bounding box size of each object divided by the frame size.

Total area of objects: fraction of pixels covered by all object bounding boxes in a frame.

Object arrival rate: the number of new arrival objects per second.

Confidence score: the confidence score of each detected object given by the full DNN.

Object count: the number of objects per frame.

Sample Pipelines