Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or fifinance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

/pdf/a-survey-on-the-explainability-of-supervised-machine-4vsxbwu6aa.pdf

A Survey on the Explainability of Supervised Machine Learning

A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. This model functions as a low level process for a car tracker. A particle filter is employed as a stochastic filter for the car tracker. The use of a particle filter allows the incorporation of the information from the low level process via importance sampling. A novel observation density for the particle filter which models the statistical dependence of neighboring pixels based on a Markov random field is presented. The effectiveness of both the low level process and the observation likelihood are demonstrated.

A probabilistic background model for tracking

https://hal.archives-ouvertes.fr/hal-01724322/document

New Trends on Moving Object Detection in Video Images Captured by a moving Camera: A Survey

We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet. Unlike previous efforts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by a fusion module. The two branches localize potential manipulation regions via visual artifacts and copy-move regions via visual similarities, respectively. To the best of our knowledge, this is the first CMFD algorithm with discernibility to localize source/target regions. We also propose simple schemes for synthesizing large-scale CMFD samples using out-of-domain datasets, and stage-wise strategies for effective BusterNet training. Our extensive studies demonstrate that BusterNet outperforms state-of-the-art copy-move detection algorithms by a large margin on the two publicly available datasets, CASIA and CoMoFoD, and that it is robust against various known attacks.

/pdf/busternet-detecting-copy-move-image-forgery-with-source-12ihdfio6r.pdf

BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

Many researchers are getting attracted in the field of object tracking in video surveillance, which is an important application and emerging research area in image processing. Video tracking is the process of locating a moving object or multiple objects over a time using camera. Due to key features of video surveillance, it has a variety of uses like human-computer interactions, security and surveillance, video communication, traffic control, public areas such as airports, underground stations, mass events, etc. Tracking a target in a cluttered premise is still one of the challenging problems of video surveillance. A sequential flow of moving object detection, its classification, tracking and identifying the behavior completes the processing framework of video surveillance. This paper takes insight into tracking methods, their categorization into different types, focuses on important and useful tracking methods. In this paper, we provide a brief overview of tracking strategies like region based, active contour based, etc with their positive and negative aspects. Different tracking methods are mentioned with detailed description. We review general strategies under literature survey on different techniques and finally stating the analysis of possible research directions.

Image processing techniques for object tracking in video surveillance- A survey

The ongoing research on object tracking in video sequences has attracted many researchers. Detecting the objects in the video and tracking its motion to identify its characteristics has been emerging as a demanding research area in the domain of image processing and computer vision. This paper proposes a literature review on the state of the art tracking methods, categorize them into different categories, and then identify useful tracking methods. Most of the methods include object segmentation using background subtraction. The tracking strategies use different methodologies like Mean-shift, Kalman filter, Particle filter etc. The performance of the tracking methods vary with respect to background information. In this survey, we have discussed the feature descriptors that are used in tracking to describe the appearance of objects which are being tracked as well as object detection techniques. In this survey, we have classified the tracking methods into three groups, and a providing a detailed description of representative methods in each group, and find out their positive and negative aspects.

/pdf/a-survey-on-moving-object-tracking-in-video-152aqzvwws.pdf

A survey on moving object tracking in video

With the advancement of image editing tools in today's world, the manipulation of images like cropping, cloning, resizing, etc., becomes an easy proposition and on the other end, checking or determining whether an image has been manipulated or not, becomes a great challenge. Copy-move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The detection of copy-move forgery has become a prominent research area. This study presents a detailed review and critical discussions with pros and cons of each of copy-move forgery detection techniques from 2007 to 2017. This study also addresses the variation in databases, issues, challenges, future directions and references in this domain.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0441

CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection

Non-Invertible cancellable fingerprint template for fingerprint biometric

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

/pdf/a-genetic-algorithm-and-fuzzy-logic-approach-for-video-shot-1l2b5i3117.pdf

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

Copy-move forgery in images is a popular tampering method, in which a portion of an image is copied and pasted on some other location of the same image. This paper proposes an enhancement of block based copy-move forgery detection using hybrid local features extraction. In this system, the image is divided into non-overlapping blocks and SURF features are computed from each block. SURF features are matched using 2NN procedure and large blocks are formed by considering the eight neighboring blocks of each SURF features match block. Maximally Stable Extremal Regions are detected from each large region and the extracted SURF descriptors from these regions are compared for matching. Finally, affine transform is applied to remove the outliers. The proposed system is experimented using MICC-F220, MICC-F2000 and MICC-F600 benchmark datasets and it yields better performance in comparison with state of the art techniques.

Dalton Meitei Thounaojam

Papers

A survey on moving object tracking in video

CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection

Non-Invertible cancellable fingerprint template for fingerprint biometric

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features