Unattended Objects Research Articles

The visible gorilla: Unexpected fast—not physically salient—Objects are noticeable

It is widely believed that observers can fail to notice clearly visible unattended objects, even if they are moving. Here, we created parametric tasks to test this belief and report the results of three high-powered experiments (total n = 4,493) indicating that this effect is strongly modulated by the speed of the unattended object. Specifically, fast-but not slow-objects are readily noticeable, whether they are attended or not. These results suggest that fast motion serves as a potent exogenous cue that overrides task-focused attention, showing that fast speeds, not long exposure duration or physical salience, strongly diminish inattentional blindness effects.

Expectation-based blindness: Predictions about object categories gate awareness of focally attended objects.

Selective attention gates access to conscious awareness, resulting in surprising failures to notice clearly visible but unattended objects ('inattentional blindness'). Here, we demonstrate that expectations can have a similar effect, even for fully attended objects ('expectation-based blindness'). In three experiments, participants (N = 613) were presented with rapid serial visual presentation (RSVP) streams at fixation and had to identify a target object indicated by a cue. Target category was repeated for the first 19 trials but unexpectedly changed on trial 20. The probability of correct target reports on this surprise trial was substantially lower than on preceding and subsequent trials. This impairment was present for switches between target letters and digits, and also for changes between human and animal face images. In contrast, no drop in accuracy was observed for novel target objects from the same category as previous targets. These results demonstrate that predictions about object categories affect visual awareness. Objects that are task relevant and focally attended often fail to get noticed when their category changes unexpectedly.

Abandoned Object Detection

In the current fast paced life, security has become an important aspect which cannot be ignored. One of the usually neglected but important division of security in public areas involves the unattended objects. Bombs and other harmful objects that are incendiary cause great harm. And in most of the cases such objects are left unattended for some time before causing harm. As such, unattended objects should be given importance and must be checked to ensure safety. This paper shows a method to identify unattended objects in public areas. This is done by comparing a fixed/initial background frame with frames after a fixed interval. If there is a change in the frames, then that refers to an attended object. No change observed, proves an unattended object which is highlighted by a box. This is done with the help of blob analysis. This paper does this with the help of MATLAB. MATLAB being a software accessible to wide range of people provides an easy process with moderate results. It uses a wide range of predefined functions and toolboxes to do this.

Oscillations in the central brain of Drosophila are phase locked to attended visual features

Object-based attention describes the brain's capacity to prioritize one set of stimuli while ignoring others. Human research suggests that the binding of diverse stimuli into one attended percept requires phase-locked oscillatory activity in the brain. Even insects display oscillatory brain activity during visual attention tasks, but it is unclear if neural oscillations in insects are selectively correlated to different features of attended objects. We addressed this question by recording local field potentials in the Drosophila central complex, a brain structure involved in visual navigation and decision making. We found that attention selectively increased the neural gain of visual features associated with attended objects and that attention could be redirected to unattended objects by activation of a reward circuit. Attention was associated with increased beta (20- to 30-Hz) oscillations that selectively locked onto temporal features of the attended visual objects. Our results suggest a conserved function for the beta frequency range in regulating selective attention to salient visual features.

Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding

Unattended object detection systems have seen full applications in military surveillance, object recognition, and intrusion prevention. When applied to actual work scenarios, these systems have problems such as low recognition accuracy, low positioning accuracy, and weak detection effect of distant objects. Obtainment of enough feature information concerning the effective signals is critical to target recognition. This work focuses on interference in seismic signals and the way to store the feature information of effective signals. First, the authors analyzed the frequency and attenuation characteristics of seismic waves of typical target sites, in which the Rayleigh wave is suitable for the detection of the energy of seismic signals produced by human targets and vehicles. As seismic signals are low-frequency waves, the authors researched the performance of the empirical mode decomposition method and the wavelet thresholding method in denoising seismic signals, and an improved empirical mode decomposition-wavelet threshold denoising method is proposed. The test result shows that the improved denoising method can effectively remove noise in seismic signals and preserve the effective signals of the target.

An Approach for Unattended Object Detection through Contour Formation using Background Subtraction

Abstract Any unattended object in public places is generally considered as a suspicious object. Identification of such objects in public places is one of challenging tasks in computer vision. Robust object detection system with prompt and accurate identification of safety breaches augments security measures at public places such as shopping malls, airports, railway stations, etc. This paper proposes a new and efficient approach for the detection of unattended objects in a well illuminated environment from video captured through a single camera. The proposed approach extracts foreground objects using background subtraction method. These foreground objects are further categorized into static and moving objects by comparing their respective coordinates of centroid in consecutive frames. The foreground object is classified as an unattended object if that object is static over a predefined period of time and its size lies in the predefined range. The performance of the proposed approach is evaluated by conducting the experiments on ABandoned Objects DAtaset (ABODA) dataset and also on few real time videos recorded by the authors. The results are derived for evaluating the experiments using Correct Object Detection Rate (CODR), Object Success Rate (OSR), and False Alarm Rate (FAR) metrics. The CODR & OSR rates so attained for the proposed approach with static background are 100% each while FAR is 0%. Average CODR, average OSR, and average FAR of static and dynamic background are found to be 70.83%, 67.25%, and 35.41% respectively for the proposed approach. The proposed approach updates/replaces the original background frame to minimize the scope of false alarm rate in case of dynamic background. It is generic and can be efficiently used for object detection in various public scenarios.

Parallel attentional facilitation of features and objects in early visual cortex.

Selective attention can enhance the processing of attended features across the entire visual field. Attention also spreads within objects, enhancing all internal locations and task‐irrelevant features of selected objects. Here, we examine the extent to which attentional enhancement of a feature spreads across attended and unattended objects. Two fully overlapping counter‐rotating bicolored surfaces of light and dark random dots were presented on a gray background of intermediate luminance. This stimulus creates a percept of two separate semitransparent surfaces and allows the measurement of feature‐ and object‐based selections while controlling spatial attention. On each trial, human participants attended to a subset of dots defined by feature (luminance polarity) and object (surface) in order to detect brief episodes of radial motion while ignoring any events in the unattended groups of dots. Attentional selection was assessed by means of steady‐state visual evoked potentials (SSVEPs) and behavioral measures. SSVEP amplitudes recorded at medial occipital electrode sites were modulated both by surface‐based and luminance polarity‐based selection in a manner consistent with independent multiplicative enhancement of attentional effects in different dimensions in early visual cortex. This finding supports the view that feature‐based attention spreads across object boundaries, at least at an early stage of processing. However, SSVEPs elicited at more lateral electrode sites showed a hierarchical pattern of selection, potentially reflecting the binding of surface‐defining features with luminance features to enable surface‐based attention.

Computer Vision based System to Detect Abandoned Objects

Security has become the most essential aspect in every walk of life. With ever growing technology, there has been an apparent inclination towards developing smart surveillance systems that do not stop with just monitoring and recording of events, but also possesses the ability to observe the events and alert in case of any discrepancies if observed. One of the impending threats to security in crowded places is the ignorance of un-attended objects. These un-attended objects or abandoned objects may have been left behind intentionally and may contain hazardous things which can cause huge disasters. This paper is focused towards developing a computer vision based approach that analyses the blob areas to detect any abandoned objects and instantaneously send appropriate alert without any human intervention. The uniqueness of this approach is that it handles the occlusion scenario and also quick execution time to detect abandoned objects. The approach has been tested with various benchmark video datasets and real-time video sequences as well. The performance has been measured in terms of accuracy of classifying abandoned objects in a given video sequence and also the execution time taken for computing the outcome. Results indicate good accuracy in terms of abandoned object detection under varying conditions and scenarios and also faster execution time when compared to other contemporary approaches.

Robo Car for Military Application

Military and battlefield applications are in an accelerated pace due to its demand fueled by government investment. In this paper a modern approach is presented for surveillance at border and remote areas using multifunctional robot based on Bluetooth technology and ARM Cortex M4. It will control both an autonomous and manually controlled robots using wireless technologies. The applications of this multisensory robot is used to detect human, bombs and metallic weapons at remote and war field areas and share the live feed and the location to the operator. Instead of having people get close to hazards such as unattended objects robots are used. An autonomous operation is performed to control using ultrasonic sensor acting as an obstacle detector. In this paper the robotic vehicle is designed for reconnaissance as well as surveillance under certain circumstances. The use of ARM Cortex M4 as the main controller helps in tight integration of system peripherals thereby reducing area and development costs in comparison with existing conventional wireless security robots.

How selective attention affects the detection of motion changes with peripheral vision in MOT

In sports, peripheral vision is expected to play an important role in tasks that demand distributed attention and motion-change detection. By using the Multiple-Object-Tracking (MOT) task, these demands were simulated in a well-controlled laboratory environment. Participants tracked four target out of ten moving objects (6 distractors) and pressed a button when one of the ten objects stopped. Detection rates for tracked targets were compared to detection rates of non-tracked distractors at eccentricities between 5° and 25°. The study's aim was to test how the location of attention affects peripheral motion detection. The results show a large attention effect because target stops were detected in 89 % and distractor stops only in 55 % of the trials. Distractor stops were more likely detected when they occurred closer to the fovea while target stops were detected at all eccentricities. That means, orienting attention at target objects facilitates the peripheral detection of their motion changes in monitoring tasks. Having distractors closer to the fovea increases the chance to also detect motion changes of unattended objects. On a theoretical level, results support a tracking mechanism with object-based attention, serial covert attention shifts and flexible but limited attentional resources. On a practical level, sports' experts should use their extensive knowledge to locate attention on most-relevant objects and reduce the eccentricity to other objects to detect motion changes of attended and unattended objects.

Emotional enhancement of memory for neutral information: The complex interplay between arousal, attention, and anticipation

It can be challenging to explain why certain mundane details circumstantial to an emotional event are nonetheless remembered long after the experience. Here, we examined how attention selectively shapes memory for neutral objects that happen to coincide with either an unexpected or anticipated emotional event. Pictures of neutral objects were presented for 2 s and terminated with either a high-intensity shock, a low-intensity shock, or no shock. Recognition memory was tested 24 -hs later in a surprise test. Results showed no effect of shock intensity on memory for attended objects when shocks were unpredictable (Experiment 1). Similarly, there was no effect of shock intensity for attended objects when shock delivery was signaled before the object appeared (Experiment 2). There was a reduction in memory for unattended objects paired with an anticipated high-intensity shock (Experiment 3). Finally, subjects recognized slightly more attended objects paired with a high-intensity shock if shock intensity was signaled one second after the object was encoded (Experiment 4). We conclude that simply pairing objects with high-intensity shocks is insufficient to drive episodic memory enhancements for neutral information. But anticipation of an impending source of arousal can induce bidirectional effects: attending to an impending emotional event interferes with encoding of neutral information, but encoding an object just prior to anticipation of an emotional event can sometimes benefit memory. Overall, these results highlight a complex interplay between arousal, attention, and anticipation on emotion-induced memory for neutral information.

Dynamic background modeling using deep learning autoencoder network

Background modeling is a major prerequisite for a variety of multimedia applications like video surveillance, traffic monitoring, etc. Numerous approaches have been proposed for the same over the past few decades. However, the need for real time artificial intelligent based low cost approach still exists. Moreover, few recently proposed efficient approaches are not validated on the basis of some of the challenging applications in which they may fail in its efficiency when tested. In this paper, an efficient deep learning technique based on autoencoder network is used for modeling the background. The background model generated herein is obtained by training the incoming frames of the surveillance video with the deep learning network in an unsupervised manner. In order to optimize the weights of the network, greedy layer wise pre-training approach is used initially and the fine tuning of the network is done using conjugate gradient based back propagation algorithm. The performance of the algorithm is validated based on the application of unattended object detection in a dynamic environment. Comprehensive assessment of the proposed method using CDNET 2014 dataset and other datasets demonstrates the efficiency of the technique in background modeling.

Suppression in object-based attention

ABSTRACTThis study examines suppression in object-based attention in three experiments using an object-based attention task similar to Egly, R., Driver, J., & Rafal, R. D. (1994. Shifting visual attention between objects and locations: Evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General, 123(2), 161–177. doi:10.1037/0096-3445.123.2.161) with the addition of a distractor. In Experiment 1 participants identified a target object at one of four ends of two rectangles. The target location was validly cued on 72% of trials. The remaining 28% were located on the same or a different object. Sixty-eight percent of trials also included a distractor on one of the two objects. Experiment 1 failed to show suppression when a distractor was present, but did demonstrate the spread of attention across the attended object when no distractor was present. Experiment 2 added a mask to the paradigm to make the task more difficult and engage suppression. When suppression was engaged in the task, data showed suppression on the unattended (different) object, but not on the attended (same) object. Experiment 3 replicated findings from Experiment 1 and 2 using a within participants design. Findings are discussed in relation to the role of suppression in visual selective attention.

A Statistical Background Modeling Algorithm for Real-Time Pixel Classification

This paper introduces a statistical background pixel classifier intended for real-time and low-resource implementation. The algorithm works within a smart video surveillance application aimed to detect unattended objects in images with fixed backgrounds. The algorithm receives an input image and builds an initial background model based on image statistics. Using this information, the algorithm identifies new objects that do not belong to the original image. The algorithm categorizes image pixels in four possible classes: shadows, midtones, highlights and foreground pixels. The classification stage produces a binary mask where only objects of interest are shown. The pixel classifier processes Quarter VGA (320 x 240) gray-scale images at a nomial processing rate of 30 frames per second. Higher resolutions such as VGA (640 x 480) have been also tested. We compare results with traditional statistical background modeling methods. Our experiments demonstrate that our approach achieves successful background segmentation at a minimal resource consumption while maintaining real-time execution.

Abandoned or removed object detection from visual surveillance: a review

Intelligent Visual Surveillance is an important and challenging research field of image processing and computer vision. To prevent the ecological and economical losses from bomb blasting, an intelligent visual surveillance is required to keep an eye on public areas, infrastructures and discriminate an unattended object left among multiple objects at public places. An unattended object without its owner since a long time at public place is considered as an abandoned object. Identification of an abandoned object on real-time can prevent the terrorists attack through an automated video surveillance system. In recent decade, a good number of publications have been presented in the field of intelligent visual surveillance to identify the abandoned or removed objects. Furthermore, few surveys can be seen in the literature for the various human activity recognition but none of them focused deeply on abandoned or removed object detection in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of abandoned or removed object detection from the surveillance videos in the last decade. We include a brief introduction of the abandoned object detection with its issues and challenges. To acknowledge to the new researchers of this field, core technologies, and frequently used general steps to recognize abandoned or removed objects have been discussed in the literature such as foreground extraction, static object detection based on non-tracking or tracking approaches, feature extraction, classification and activity analysis to recognize abandoned object. The objective of this paper is to provide the literature review in the field of abandoned or removed object recognition from visual surveillance systems with its general framework to the researchers of this field.

Timing of repetition suppression of event-related potentials to unattended objects.

Current theories of object perception emphasize the automatic nature of perceptual inference. Repetition suppression (RS), the successive decrease of brain responses to repeated stimuli, is thought to reflect the optimization of perceptual inference through neural plasticity. While functional imaging studies revealed brain regions that show suppressed responses to the repeated presentation of an object, little is known about the intra‐trial time course of repetition effects to everyday objects. Here, we used event‐related potentials (ERPs) to task‐irrelevant line‐drawn objects, while participants engaged in a distractor task. We quantified changes in ERPs over repetitions using three general linear models that modeled RS by an exponential, linear, or categorical “change detection” function in each subject. Our aim was to select the model with highest evidence and determine the within‐trial time‐course and scalp distribution of repetition effects using that model. Model comparison revealed the superiority of the exponential model indicating that repetition effects are observable for trials beyond the first repetition. Model parameter estimates revealed a sequence of RS effects in three time windows (86–140, 322–360, and 400–446 ms) and with occipital, temporoparietal, and frontotemporal distribution, respectively. An interval of repetition enhancement (RE) was also observed (320–340 ms) over occipitotemporal sensors. Our results show that automatic processing of task‐irrelevant objects involves multiple intervals of RS with distinct scalp topographies. These sequential intervals of RS and RE might reflect the short‐term plasticity required for optimization of perceptual inference and the associated changes in prediction errors and predictions, respectively, over stimulus repetitions during automatic object processing.

An Enhanced Image Hashing to Detect Unattended Objects Utilizing Binary SVM Classification

An Enhanced Image Hashing to Detect Unattended Objects Utilizing Binary SVM Classification

Decoding early and late cortical contributions to individuation of attended and unattended objects

To isolate a visual stimulus as a unique object with a specific spatial location and time of occurrence, it is necessary to first register (individuate) the stimulus as a distinct perceptual entity. Recent investigations into the neural substrates of object individuation have suggested it is subserved by a distributed neural network, but previous manipulations of individuation load have introduced extraneous visual confounds, which might have yielded ambiguous findings, particularly in early cortical areas. Furthermore, while it has been assumed that selective attention is required for object individuation, there is no definitive evidence on the brain regions recruited for attended and ignored objects. Here we addressed these issues by combining functional magnetic resonance imaging (fMRI) with a novel object-enumeration paradigm in which to-be-individuated objects were defined by illusory contours, such that the physical elements of the display remained constant across individuation conditions. Multi-voxel pattern analyses revealed that attended objects modulated patterns of activity in early visual cortex, as well as frontal and parietal brain areas, as a function of object-individuation load. These findings suggest that object individuation recruits both early and later cortical areas, consistent with theoretical accounts proposing that this operation acts at the junction of feed-forward and feedback processing stages in visual analysis. We also found dissociations between brain regions involved in individuation of attended and unattended objects, suggesting that voluntary spatial attention influences the brain regions recruited for this process.

High-Fidelity Visual Long-Term Memory within an Unattended Blink of an Eye.

What is stored in long-term memory from current sensations is a question that has attracted considerable interest. Over time, several prominent theories have consistently proposed that only attended sensory information leaves a durable memory trace whereas unattended information is not stored beyond the current moment, an assumption that seems to be supported by abundant empirical evidence. Here we show, by using a more sensitive memory test than in previous studies, that this is actually not true. Observers viewed a rapid stream of real-world object pictures overlapped by words (presentation duration per stimulus: 500 ms, interstimulus interval: 200 ms), with the instruction to attend to the words and detect word repetitions, without knowing that their memory would be tested later. In a surprise two-alternative forced-choice recognition test, memory for the unattended object pictures was tested. Memory performance was substantially above chance, even when detailed feature knowledge was necessary for correct recognition, even when tested 24 h later, and even although participants reported that they do not have any memories. These findings suggests that humans have the ability to store at high speed detailed copies of current visual stimulations in long-term memory independently of current intentions and the current attentional focus.

The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study.

At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem.