شماره ركورد كنفرانس :
3297
عنوان مقاله :
Locally Anomaly Detection in Crowded Scenes Using Locality Constrained Linear Coding
عنوان به زبان ديگر :
Locally Anomaly Detection in Crowded Scenes Using Locality Constrained Linear Coding
پديدآورندگان :
Yousefi Hajar Shiraz University Iran , Nazemi Azadeh Shiraz University Iran , Azimifar Zohreh Shiraz University Iran
كليدواژه :
Crowded scenes , ( Locality-constrained Linear Coding (LLC , sparse representation , video anomaly detection
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
The investigation surrounding recent Stockholm
and New York terrorist attack enforced this research to emphasize
on anomaly detection. This paper describes the main part of an
ongoing study through anomaly detection and localization which
aims to improve anomaly localization accuracy. The sparsity
constraint used in most recent anomaly detection researches
is replaced with Locality-constrained Linear Coding. Localityconstrained
Linear Coding (LLC) reconstruction cost criterion
is designed to detect anomalies that occur in video locally.
Implementing this method, the obtained experimental results
approves considerable improvement regarding localization.