Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Orts-Escolano, Sergio; García-Rodríguez, José; Morell, Vicente; Cazorla, Miguel; Molina‐Azorín, José F.; García‐Chamizo, Juan Manuel

doi:10.3390/jsan3020095

Cited by 5 publications

(2 citation statements)

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“…Existing work on the same dataset either employs clustering algorithms with GPU optimisation [25], or focusses on motion analysis by matching trained silhouette models [28]. We differ significantly from these previous works: we developed an accurate tracking framework in which we can employ a highly accurate pedestrian detector on these challenging images, and thus perform much better than existing methods.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Detection and Tracking in Challenging Surveillance Videos

Beeck

Goedemé

2016

Communications in Computer and Information Science

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In this chapter we propose a novel approach for real-time robust pedestrian tracking in surveillance images. Typical surveillance images are challenging to analyse since the overall image quality is low (e.g. low resolution and high compression). Furthermore often birds-eye viewpoint wide-angle lenses are used to achieve maximum coverage with a minimal amount of cameras. These specific viewpoints make it unfeasible to directly apply existing pedestrian detection techniques. Moreover, real-time processing speeds are required. To overcome these problems we introduce a pedestrian detection and tracking framework which exploits and integrates these scene constraints to achieve high accuracy results. We performed extensive experiments on publically available challenging real-life video sequences concerning both speed and accuracy. Our approach achieves excellent accuracy results while still meeting the stringent real-time demands needed for these surveillance applications, using only a single-core CPU implementation.

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Section: Related Workmentioning

confidence: 99%

Pedestrian Detection and Tracking in Challenging Surveillance Videos

Beeck

Goedemé

2016

Communications in Computer and Information Science

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“…These images are of low-resolution, low-quality and, due to the use of wide-angle lenses show large amounts of distortion and contain non-trivial viewpoints. Existing work on the same dataset either employs clustering algorithms with GPU optimisation (Orts-Escolano et al, 2014), or focusses on motion analysis by matching trained silhouette models (Rogez et al, 2014a). We differ significantly from these previous works: we developed an accurate tracking framework in which we can employ a highly accurate pedestrian detector on these challenging images, and thus perform much better than existing methods.…”

Section: Related Workmentioning

confidence: 99%

Real-time Accurate Pedestrian Detection and Tracking in Challenging Surveillance Videos

Beeck

Goedemé

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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This paper proposes a novel approach for real-time robust pedestrian tracking in surveillance images. Such images are challenging to analyse since the overall image quality is low (e.g. low resolution and high compression). Furthermore often birds-eye viewpoint wide-angle lenses are used to achieve maximum coverage with a minimal amount of cameras. These specific viewpoints make it difficult -or even unfeasible -to directly apply existing pedestrian detection techniques. Moreover, real-time processing speeds are required. To overcome these problems we introduce a pedestrian detection and tracking framework which exploits and integrates these scene constraints to achieve excellent accuracy results. We performed extensive experiments on challenging real-life video sequences concerning both speed and accuracy. We show that our approach achieves excellent accuracy results while still meeting the stringent real-time demands needed for these surveillance applications, using only a single-core CPU implementation.

show abstract