Bilattice-based Logical Reasoning for Human Detection (original) (raw)

Recursive Context Reasoning for Human Detection and Parts Identification

2000

Human detection and body parts identi cation are important and challenging problems in computer vision. High performance human detection depends on reliable contour extraction, but contour extraction is an under constrained problem without the knowledge about the objects to be detected. This paper proposes a recursive context reasoning (RCR) approach to solving the above dilemma. A TRS-invariant probabilistic model is designed to encode the shapes of the body parts and the context information | the size and spatial relationships between body parts. A Bayesian framework is developed to perform human detection and part identi cation under partial occlusion. A contour reconstruction procedure is introduced to integrate the human model and the identi ed body parts to predict the shapes and locations of the parts missed by the contour detector; the re ned contours are used to reevaluate the likelihood ratio. Therefore, contour extraction, part identi cation, and human detection are impro...

Meeting in the Middle: A top-down and bottom-up approach to detect pedestrians

This paper proposes a generic approach combining a bottom-up (low-level) visual detector with a topdown (high-level) fuzzy first-order logic (FOL) reasoning framework in order to detect pedestrians from a moving vehicle. Detections from the low-level visual corner based detector are fed into the logical reasoning framework as logical facts. A set of FOL clauses utilising fuzzy predicates with piecewise linear continuous membership functions associates a fuzzy confidence (a degree-of-truth) to each detector input. Detections associated with lower confidence functions are deemed as false positives and blanked out, thus adding top-down constraints based on global logical consistency of detections. We employ a state of the art visual detector on a challenging pedestrian detection dataset, and demonstrate an increase in detection performance when used in a framework that combines bottom-up detections with (fuzzy FOL-based) top-down constraints.

Abandoned Object Detection with Logical Reasoning

2014 IEEE International Advance Computing Conference (IACC), 2014

Abandoned object detection is an essential requirement in many video surveillance contexts. We introduce an abandoned object detection tool based on a set of possible events and on a set of rules to act upon those events. This implementation is simple and reusable unlike existing techniques. It is implemented using a simple logical reasoning upon textual data, in contrast to image centric processing. Objects foreign to a usual environment are extracted using background subtraction. Results of blob detection and tagging process are passed to an abandoned object detector in a textual format. The abandoned object detector, which is an acyclic graph of asynchronously interconnected lightweight processing modules, evaluates the variations of speeds and inter-blob distances. By configuring several parameters according to the context, it generates an alert upon encountering such a scenario. We provide results of this implementation by applying it on PETS 2006 dataset.

Object-Oriented Logic Programming of Intelligent Visual Surveillance for Human Anomalous Behavior Detection

2019

The idea of the logic programming-based approach to the intelligent visual surveillance is in usage of logical rules for description and analysis of people behavior. New prospects in logic programming of the intelligent visual surveillance are connected with the usage of 3D machine vision methods and adaptation of the multi-agent approach to the intelligent visual surveillance. The main advantage of usage of 3D vision instead of the conventional 2D vision is that the first one can provide essentially more complete information about the video scene. The availability of exact information about the coordinates of the parts of the body and scene geometry provided by means of 3D vision is a key to the automation of behavior analysis, recognition, and understanding. This chapter supplies the first systematic and complete description of the method of object-oriented logic programming of the intelligent visual surveillance, special software implementing this method, and new trends in the re...

Tracking interacting objects in complex situations by using contextual reasoning

In this paper we propose a novel real-time tracking algorithm robust with respect to several common errors occurring in object detection systems, especially in the presence of total or partial occlusions. The algorithm takes into account the history of each object, whereas most other methods base their decisions on only the last few frames. More precisely, it associates each object with a state encoding the relevant information of its past history, that enable the most appropriate way of assigning an identity to the object on the basis of its current and past conditions. Thus, strategies that are more complex but also riskier are only applied when the algorithm is confident that is appropriate to do so. An experimental evaluation of the algorithm has been performed using the PETS2010 database, comparing the obtained performance with the results of the PETS 2010 contest participants.

Semantic Annotation of Complex Human Scenes for Multimedia Surveillance

2007

A Multimedia Surveillance System (MSS) is considered for automatically retrieving semantic content from complex outdoor scenes, involving both human behavior and traffic domains. To characterize the dynamic information attached to detected objects, we consider a deterministic modeling of spatio-temporal features based on abstraction processes towards fuzzy logic formalism. A situational analysis over conceptualized information will not only allow us to describe human actions within a scene, but also to suggest possible interpretations of the behaviors perceived, such as situations involving thefts or dangers of running over. Towards this end, the different levels of semantic knowledge implied throughout the process are also classified into a proposed taxonomy.

Common-sense reasoning for human action recognition

Pattern Recognition Letters, 2013

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm -known as bag of words -gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline.

Human Detection in Video Surveillance

International Journal of Applied Sciences and Smart Technologies, 2021

Recognition of the human activities in videos has gathered numerous demands in various applications of computer vision like Ambient Assisted Living, intelligent surveillance, Human-Computer interaction. One of the most pioneering techniques for Human Detection in Video Surveillance based on deep learning and this project mainly focuses on various approaches based on that. This paper provides an idea of solution to use video surveillance more effectively, by detecting any humans present and notifying the concerned people. The deep learning model, preferred for fast computation, Convolution Neural Network is used by stacking 3 blocks of layers on fully connected layers. This provided an identification of humans and naïve approach to eliminate inanimate human like objects such as mannequins.

Finding the weakest link in person detectors

Computer Vision and Pattern …, 2011

Detecting people remains a popular and challenging problem in computer vision. In this paper, we analyze parts-based models for person detection to determine which components of their pipeline could benefit the most if improved. We accomplish this task by studying numerous detectors formed from combinations of components performed by human subjects and machines. The parts-based model we study can be roughly broken into four components: feature detection, part detection, spatial part scoring and contextual reasoning including non-maximal suppression. Our experiments conclude that part detection is the weakest link for challenging person detection datasets. Non-maximal suppression and context can also significantly boost performance. However, the use of human or machine spatial models does not significantly or consistently affect detection accuracy.

Multivalued Default Logic for Identity Maintenance in Visual Surveillance

Lecture Notes in Computer Science, 2006

Recognition of complex activities from surveillance video requires detection and temporal ordering of its constituent "atomic" events. It also requires the capacity to robustly track individuals and maintain their identities across single as well as multiple camera views. Identity maintenance is a primary source of uncertainty for activity recognition and has been traditionally addressed via different appearance matching approaches. However these approaches, by themselves, are inadequate. In this paper, we propose a prioritized, multivalued, default logic based framework that allows reasoning about the identities of individuals. This is achieved by augmenting traditional appearance matching with contextual information about the environment and self identifying traits of certain actions. This framework also encodes qualitative confidence measures for the identity decisions it takes and finally, uses this information to reason about the occurrence of certain predefined activities in video.