SFU at TRECVid 2010: Surveillance Event Detection

SFU at TRECVid 2009: Event Detection

This paper describes the SFU entry for the TRECVid 2009 event detection challenge. We attempted to detect events involving either one or two individuals, and present algorithms for “running”, “embrace”, and “pointing” event detection. The system has three stages. First, preprocessing is performed to extract candidate space-time regions using background subtraction and photogrammetric context. T...

NHK STRL at TRECVID 2010: Semantic Indexing and Surveillance Event Detection

INRIA-WILLOW at TRECVID 2010 : Surveillance Event Detection

This notebook paper presents a system evaluated in the Surveillance Event Detection (SED) task of TRECVid 2010 campaign. We investigate a generic statistical approach applied to seven event classes defined by the SED task. Our video representation is based on local space-time descriptors which are vectorquantized and aggregated into histograms within short temporal windows and spatial regions d...

'Person Runs' detection for the TRECVID Surveillance Event Detection task

We discuss an event detection approach based on local feature modeling, which is based on spatio-temporal cuboids and perspective normalization (QMUL-ACTIVA 3 / p-baseline 1). Motion information is compared against examples of events learned from a training dataset to define a similarity measure. This similarity measure is then analyzed in both space and time to identify frames containing insta...

Toshiba at TRECVID 2008: Surveillance Event Detection Task

In this paper, we describe the Toshiba event detection system for TRECVID surveillance event detection task [1] that detects three TRECVID required events (E05:PersonRuns, E19:ElevatorNoEntry, and E20:OpposingFlow). Our system (“Toshiba_1 p-cohog_1”) consists of four components: (1) robust change detection based on the combination of pixel intensity histogram, PTESC (Peripheral TErnary Sign Cor...