Traditional Approaches in Background Modeling for Static Cameras

Bouwmans, Thierry

doi:10.1201/b17223-3

Cited by 8 publications

(6 citation statements)

References 254 publications

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“…We used the F-measure to evaluate the degree of correctness exhibited by figure-ground discrimination because the F-measure is an evaluation metric for assessing classifier performance in a two-class classification problem. The F-measure is especially convenient for evaluating motion detection methods [ 81 ].…”

Section: Methodsmentioning

confidence: 99%

Bioinspired figure-ground discrimination via visual motion smoothing

Guo

2023

PLoS Comput Biol

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Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

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Section: Methodsmentioning

confidence: 99%

Bioinspired figure-ground discrimination via visual motion smoothing

Guo

2023

PLoS Comput Biol

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“…Lateral inhibition (LI) is the basis of many discrete-time recurrent networks. Moreover, the lateral inhibition method and its application to motion detection tasks have been exploited in various computer vision applications [ 8 ]. A previous work by the same authors introduced a finite state machine version of the so-called algorithmic lateral inhibition (ALI) method, implemented in field programmable gate arrays (FPGAs) in order to detect moving objects in video [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

López

Bermúdez

Montero

et al. 2018

Sensors

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Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.

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“…Traditionally, BS methods model the background using color, intensity or gradients for each pixel using mixture of Gaussians [2], k-nearest neighbor or other classifiers to become invariant to small changes in illumination and moving shadows [3,4,5]. BS is intended for static cameras and for detecting moving or appearing objects in a video sequence.…”

Section: Introductionmentioning

confidence: 99%

“…BS is intended for static cameras and for detecting moving or appearing objects in a video sequence. For a moving camera, the low level features used by conventional BS struggle to model a moving background [5], and many moving camera applications detect obstacles using general object detection algorithms or depth sensors.…”

Section: Introductionmentioning

confidence: 99%

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Christiansen

Nielsen²,

Steen³

et al. 2016

Sensors

128

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Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

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Traditional Approaches in Background Modeling for Static Cameras

Cited by 8 publications

References 254 publications

Bioinspired figure-ground discrimination via visual motion smoothing

Bioinspired figure-ground discrimination via visual motion smoothing

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

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