Reidentification by Relative Distance Comparison
Sun Yat-sen University · Queen Mary University of London
Abstract
Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a…
Citation impact
- FWCI
- 44.97
- Percentile
- 100%
- References
- 47
Authors
3Topics & keywords
- Artificial intelligence
- Clutter
- Computer science
- Similarity (geometry)
- Matching (statistics)
- Measure (data warehouse)
- Range (aeronautics)
- Pattern recognition (psychology)
- Reduced inequalities