Moving Object Detection in Highly Corrupted Noise using Analysis of Variance
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 6
Abstract
This paper implements three-way nested design to mark moving objects in a sequence of images. Algorithm performs object detection in the image motion analysis. The inter-frame changes (level-A) are marked as temporal contents, while the intra-frame variations identifies critical information. The spatial details are marked at two granular levels, comprising of level-B and level-C. The segmentation is performed using analysis of variance (ANOVA). This algorithm gives excellent results in situations where images are corrupted with heavy Gaussian noise ~N(0,100). The sample images are selected in four categories: ‘baseline’, ‘dynamic background’, ‘camera jitter’, and ‘shadows’. Results are compared with previously published results on four accounts: false positive rate (FPR), false negative rate (FNR), percentage of wrong classification (PWC), and an F-measure. The qualitative and quantitative results prove that the technique out performs the previously reported results by a significant margin.
Authors and Affiliations
Asim ur Rehman Khan, Muhammad Burhan Khan, Haider Mehdi, Syed Muhammad Atif Saleem
Keywords
Related Articles
- EP ID EP596750
- DOI 10.14569/IJACSA.2019.0100629
- Views 73
- Downloads 0
Detection of Infected Leaves and Botanical Diseases using Curvelet Transform
The study of plants is known as botany and for any botanist it is a daily routine work to examine various plants in their research lab. This research efforts an image processing-based algorithm for extracting the region...
An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN
Species classification from DNA sequences remains as an open challenge in the area of bioinformatics, which deals with the collection, processing and analysis of DNA and proteomic sequence. Though incorporation of data m...
MIMC: Middleware for Identifying & Mitigating Congestion Level in Hybrid Mobile Adhoc Network
Adoption of middleware system to solve the congestion problem in mobile ad-hoc network is few to find in the existing system. Research gap is found as existing congestion control mechanism in MANET doesn’t use middleware...
LOQES: Model for Evaluation of Learning Object
Learning Object Technology is a diverse and contentious area, which is constantly evolving, and will inevitably play a major role in shaping the future of both teaching and learning. Learning Objects are small chun...
Extending Conditional Preference Networks to Handle Changes
Conditional Preference Networks (CP-nets) are a compact and natural model to represent conditional qualitative preferences. In CP-nets, the set of variables is fixed in advance. That is, the set of alternatives available...