Video annotation is inherently more complex than image annotation, due to the painstaking and labor-intensive process of labeling moving targets frame-by-frame. Manual annotation for a huge volume of individual frames is not only time-consuming, it can also be quite expensive.
TL;DR: Sama is investing in ML-powered tools to optimize efficiency for your video annotation projects; introducing ML object tracking to increase throughput without sacrificing quality.
In recent years, a number of automated annotation tools have hit the market to optimize annotator productivity and cut labeling costs, but these solutions often come with trade-offs between increased throughput and accuracy. For example, tools like linear interpolation can increase annotation speed on objects that move in a straight line, but video data with dynamic objects will inevitably require a lot of rework on the part of human annotators — negating any potential for efficiency gains.
The good news is that there are ML-powered video annotation techniques that effectively balance labeling speed with accuracy, no trade-offs required.
ML object tracking enables greater labeling throughput for better results with less manual intervention
Sama ML object tracking for video annotation uses a single human-in-the-loop (HITL) input and machine learning extrapolation to predict and implement bounding box annotations for up to 100 subsequent frames.
ML object tracking dynamically corrects annotation errors when discrepancies between its predicted annotations and successive HITL-generated keyframes are flagged. The result is highly accurate bounding boxes for video data — even for dynamic objects.
With ML object tracking for video annotation, annotators label a single keyframe — in this case, a bounding box around a road sign — and ML extrapolation then accurately predicts and annotates up to 100 subsequent frames.
With ML object tracking for video annotation, annotators label a single keyframe — in this case, a bounding box around a road sign — and ML extrapolation then accurately predicts and annotates up to 100 subsequent frames.At Sama, we empower our annotators with best-in-class tools to increase their productivity, and ultimately get your models to market faster. Our full-time dedicated workforce of data experts work directly with you to ensure the delivery of high-quality datasets, guaranteed.
Sama’s dedicated workforce coupled with our secure and compliant annotation platform and ISO-certified delivery centers protect our customers from costly mistakes that arise from poor security protocols and lax policy enforcement.
The result? Quality data at scale for your video annotation projects, delivered on time and protected from ingestion to delivery.
Sama ML object tracking increases annotation speed while maintaining quality for greater throughput and a faster time to market.
