Multiple Object Tracking Accuracy Data for Unique Color and Shape Combinations

An experimental dataset investigating the effects of item heterogeneity and target similarity on Multiple Object Tracking accuracy. The data was created by author Rachel Eng and was last updated on April 25, 2026. Items in the experiment consisted of 16 unique combinations of four colors and four shapes.

Use Cases

• Modeling visual attention performance based on target feature similarity described in the experiment.
• Analyzing the impact of item heterogeneity on tracking accuracy as mentioned in the description.
• Training or benchmarking computational models of Multiple Object Tracking using the described experimental paradigm.

Strengths

• Experimental design is clearly described, involving 16 unique color-shape combinations.
• Last update date is explicitly provided as 2026-04-25.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Experimental data from a project investigating Multiple Object Tracking.

Time Range

Experimental timeframe is not specified.

Freshness

Last updated 2026-04-25 04:10:34; freshness should be verified.

Geography

Geographic scope is not specified.