Object tracking is an essential step of a video processing pipeline. In the Fish4Knowledge project, recognizing fish trajectories allows to provide information to higher-level modules, such as behavior understanding and population size estimation. However, video quality limitations and appearance/motion characteristics of fish make the task much more challenging than in typical human-based applications in urban contexts. To solve this problem, robust appearance and motion models must be employed: this chapter describes an approach devised to tackle the fish tracking problem in this project, and presents and evaluation of the tracking algorithm in comparison with state-of-the-art techniques.
Fish Tracking
GIORDANO, Daniela;Palazzo S;SPAMPINATO, CONCETTO
2016-01-01
Abstract
Object tracking is an essential step of a video processing pipeline. In the Fish4Knowledge project, recognizing fish trajectories allows to provide information to higher-level modules, such as behavior understanding and population size estimation. However, video quality limitations and appearance/motion characteristics of fish make the task much more challenging than in typical human-based applications in urban contexts. To solve this problem, robust appearance and motion models must be employed: this chapter describes an approach devised to tackle the fish tracking problem in this project, and presents and evaluation of the tracking algorithm in comparison with state-of-the-art techniques.File | Dimensione | Formato | |
---|---|---|---|
Fish4Knowledge_ Chap9.pdf
solo gestori archivio
Licenza:
Non specificato
Dimensione
363.42 kB
Formato
Adobe PDF
|
363.42 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.