Name
Title | ||
|---|---|---|
Modeling of temporarily static objects for robust abandoned object detection in urban surveillance |
Abstract | ||
|---|---|---|
We propose a robust approach for abandoned object detection in urban surveillance with over thousands of cameras. For such a large-scale monitoring based on intelligent video analysis, it is critical that a system be designed with careful control of false alarms. Our approach is based on proactive modeling of temporally static objects (TSO) such as cars stopping at red light and still pedestrians in the street. We develop a finite state machine to track the entire life cycles of TSOs from creation to termination. The semantically meaningful object information provided by the state machine in turn allows adaptive region-level updating of the background model without using any sophisticated object classification techniques. We demonstrate that our approach significantly mitigates the problematic issue of false alarm related to people in city surveillance, using both a small publicly available data set and a large one collected from various realistic urban scenarios. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/AVSS.2011.6027290 | AVSS |
Keywords | Field | DocType |
finite state machines,adaptive region-level updating,finite state machine,robust abandoned object detection,robust approach,abandoned object detection,background model,state machine,large-scale monitoring,temporarily static object proactive modelling,city surveillance,image classification,false alarm,cameras,object detection,object classification techniques,sophisticated object classification technique,intelligent video analysis,temporally static object,semantically meaningful object information,urban surveillance,video surveillance,robustness,life cycle,object recognition | Object detection,Computer vision,False alarm,Computer science,Robustness (computer science),Finite-state machine,Red light,Artificial intelligence,Contextual image classification,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
978-1-4577-0843-5 | 14 | 0.63 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quanfu Fan | 1 | 504 | 32.69 |
| Sharath Pankanti | 2 | 3542 | 292.65 |