Multi-target tracking on confidence maps: An application to people tracking

Poiesi, Fabio; Mazzon, Riccardo; Cavallaro, Andrea

doi:10.1016/j.cviu.2012.08.008

Cited by 53 publications

(36 citation statements)

References 35 publications

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“…For fair comparisons, the SMOT is also implemented based on the same MOT framework described in Algorithm 1 but with the self motion model. We also compare our method (RMOT) with other state-of-the-art methods, i.e., two Online methods (StructMOT [9] and MOT-TBD [17]) and four Offline methods (PRIMPT [10], OnlineCRF [24], and CemTracker [13]), we use the reported results in their paper. For new benchmark sequences (Jelmoli, Marathon1, Marathon2), we compare the proposed RMOT with the SMOT.…”

Section: Methodsmentioning

confidence: 99%

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Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Yoon¹,

Yang

Lim

et al. 2015

2015 IEEE Winter Conference on Applications of Computer Vision

175

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Online multi-object tracking with a single moving camera is a challenging problem as the assumptions of 2D conventional motion models (e.g., first or second order models) in the image coordinate no longer hold because of global camera motion. In this paper, we consider motion context from multiple objects which describes the relative movement between objects and construct a Relative Motion Network (RMN) to factor out the effects of unexpected camera motion for robust tracking. The RMN consists of multiple relative motion models that describe spatial relations between objects, thereby facilitating robust prediction and data association for accurate tracking under arbitrary camera movements. The RMN can be incorporated into various multi-object tracking frameworks and we demonstrate its effectiveness with one tracking framework based on a Bayesian filter. Experiments on benchmark datasets show that online multi-object tracking performance can be better achieved by the proposed method.

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Section: Methodsmentioning

confidence: 99%

“…The number of ground truth (GT) is reported in Table 2 and 2. We utilize the same evaluation software 2 used in the other previous papers [10,9,17,24,13] because different evaluation softwares measure performance differently [14].…”

Section: Methodsmentioning

confidence: 99%

Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Yoon¹,

Yang

Lim

et al. 2015

2015 IEEE Winter Conference on Applications of Computer Vision

175

View full text Add to dashboard Cite

show abstract

“…Target presence are recorded based on spatial position within the environment using the concept of a spatial occupancy grid [44,45]. Spatially co-occurring targets, re-detected by the same platform over multiple sensor samples or from multiple sensor platforms traversing the same region of the environment, are merged to form a single target record.…”

Section: Processing and Spatial Visualizationmentioning

confidence: 99%

Multi-modal target detection for autonomous wide area search and surveillance

et al. 2013

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Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task -the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here.

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“…Poiesi e.a. [24] proposed a method for multitarget track-before-detect based on particle filtering. They included the target ID into the particle state to handle unknown and large number of targets.…”

Section: Tracklet Generationmentioning

confidence: 99%

Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

et al. 2013

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The capability to track individuals in CCTV cameras is important for e.g. surveillance applications at large areas such as train stations, airports and shopping centers. However, it is laborious to track and trace people over multiple cameras. In this paper, we present a system for real-time tracking and fast interactive retrieval of persons in video streams from multiple static surveillance cameras. This system is demonstrated in a shopping mall, where the cameras are positioned without overlapping fields-of-view and have different lighting conditions. The results show that the system allows an operator to find the origin or destination of a person more efficiently. The misses are reduced with 37%, which is a significant improvement.

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Multi-target tracking on confidence maps: An application to people tracking

Cited by 53 publications

References 35 publications

Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Multi-modal target detection for autonomous wide area search and surveillance

Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

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