Improving shadow suppression in moving object detection with HSV color information

By Rita Cucchiara, Costantino Grana, Massimo Piccardi, Andrea Prati, Stefano Sirotti. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), 2001

488 citations recorded by the source corpus.

Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques able to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. Signal processing and optic motivations of the approach proposed are described. The integration and exploitation of the shadow detection module into the system are outlined and experimental results are shown and evaluated.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/a5286806d26d46d65c42cd1f6b71be54ff88e58c

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Citations in the corpus

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, unknown): “AND RGB - Huang and Chen (2013) [153] Cerebellar-Model-Articulation-Controller (CMAC) [153] - Threshold YCrCb Block Chen and Huang (2014) [69] PCA-based RBF Network [69] Selective Maintenance Euclidean distance YCrCb Block Lee et al (2015) [205] GDSM with Running Average [205] Selective Maintenance ”
Physical models for moving shadow and object detection in video (2004, background): “Recently, statistical-based approaches using a mixture model have shown that many problematic phenomena such as repetitive motion of the background (e.g., swaying trees), sudden illumination changes (e.g., cloud cover), and sensor noise can be modeled [4], [8], [16].”
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal (2017, background): “, shadow removal) in images is of great interest for the downstream computer vision tasks, such as efficient object detection and tracking [3, 32].”
Detecting Moving Shadows: Algorithms and Evaluation (2003, background): “In fact, a shadow cast on a background does not change its hue significantly [30].”
Detecting Moving Objects, Ghosts, and Shadows in Video Streams (2003, methodology): “In the absence of any a priori knowledge about target and environment, the most widely adopted approach for moving object detection with fixed camera is based on background subtraction [1], [2], [3], [4], [5], [6], [7], [8], [9].”
A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, methodology): “One example of this approach is discussed in [31], which is used in several traffic systems”