In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy

Camplani, Massimo

Salgado Álvarez de Sotomayor, Luis

English

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

 Abstract-- In this paper we present an adaptive multi-camera 
system for real time object detection able to efficiently adjust the 
computational requirements of video processing blocks to the 
available processing power and the activity of the scene. The 
system is based on a two level adaptation strategy that works at 
local and at global level. Object detection is based on a Gaussian 
mixtures model background subtraction algorithm. Results show 
that the system can efficiently adapt the algorithm parameters 
without a significant loss in the detection accuracy. 
I. INTRODUCTION 
Object detection algorithms are fundamental modules in all 
video based tracking systems. The analysis of temporal and 
spatial information between different frames allows the 
detection of moving objects in a video sequence. Background 
subtraction is a technique that estimates a background model 
of the scene and, then, any deviation from the model is 
considered as a moving object. Different background modeling 
techniques have been presented in the literature [1].    
Stauffer and Grimson [2] presented a popular algorithm that 
aims at estimating a background model of the scene where 
each pixel is independently modeled as a mixture of Gaussian 
distributions. The algorithm is composed by two fundamental 
steps: the first step is the estimation of whether or not a pixel 
belongs to the background model; during the second step the 
Gaussian parameters are updated. This “pixel-wise” approach 
is computational demanding since for each pixel all the 
parameters of the distributions have to be updated. 
 Several variants of this algorithm have been presented in 
the literature with the aim of reducing the computational time 
in real time applications [3]: improve the efficiency in the 
estimation of the parameters [4]; implement variable block 
size approaches, sub sampling techniques and hierarchical 
strategies [3]; or use a variable number of Gaussians per pixel 
as a function of the amount of foreground pixels [5]. 
Nevertheless, these approaches are strictly related to a specific 
application, showing a low degree of adaptability since they 
are based on the modification of only one characteristic of the 
algorithm. Their direct applicability to multi-camera systems is 
very limited, and, therefore, more efficient and adaptive 
strategies are required.   
In this work we present an adaptive and efficient multi-
camera system for real time object detection able to adapt the 
computational requirements to the scene activity and the 
available processing resources. 
 
This work has been supported by the Ministerio de Ciencia e 
Innovación of the Spanish Government under project TEC2007-
67764 (SmartVision).   
 
II. SYSTEM OVERVIEW 
The proposed system (see Fig.1) is composed by a single 
processing unit (PU) connected to several cameras. The PU 
completes three different tasks. For each camera an acquisition 
task and one processing task are present. The acquired images 
are stored in a buffer. The controller unit is a centralized 
module that collects data on the overall system performance 
and manages the parameters adaptation phase.  
The background subtraction algorithm is adaptively 
modified changing three different parameters: the number of 
Gaussian distributions per pixel; the frequency with which the 
parameters are updated and the image size: the algorithm can 
work at block-size level rather than at a pixel level. 
 
 
 
Fig. 1.  Scheme of the proposed adaptive system. 
III. MULTI-LEVEL ADAPTATION STRATEGY 
The proposed parameters adaptation policy is a two level 
strategy. The first one works at local (camera) level: it adapts 
the background model parameters independently for each 
camera. If the changes done in the first level are not sufficient,   
the second (global) level balances the processing load among 
all the tasks. The buffer occupancy level is continuously 
monitored in order to have a measurement of the processing 
needs of each task: a growing level of buffer occupancy 
corresponds to a slow processing task.   
Let us consider the ith image buffer: we define iBmax as the 
buffer size; iuflB  and
i
ovflB  as the minimum and maximum 
allowed buffer occupancy. We define ipTmax as the processing 
time of the ith processing module without any parameter 
adaptation (optimal algorithm) and ipTmin as the minimum 
processing time of the ith processing module that does not 
compromise the detection accuracy. The target of the proposed 
strategy is to adapt the algorithm performance in order to 
prevent the buffer occupancy from exceeding iuflB and 
i
ovflB . 
A. Local Level Adaptation (LLA)  
  There are two different strategies for parameters adaptation 
that depend on the scene (foreground) activity. If the scene 
 Adaptive Multi-Camera System for Real Time Object Detection 
Massimo Camplani and Luis Salgado 
Grupo de Tratamiento de Imágenes, Universidad Politécnica de Madrid, Spain 
2011 IEEE International Conference on Consumer Electronics (ICCE)
978-1-4244-8712-7/11/$26.00©2011 IEEE 797
  
shows mainly a static background, the current frame is said to 
be characterized by a Low Pixel Activity (LPA). When LPA is 
found, algorithm parameters are tuned to keep buffer 
occupancy close to iuflB . First of all, the number of Gaussians 
used and the parameters update rate are decreased: in fact, a 
static background can be modeled with a smaller number of 
Gaussians without decreasing the detection accuracy and a fast 
update rate is not necessary. Finally, if buffer occupancy does 
not decrease to the desired level, spatial sub-sampling is 
applied. When moving objects have been detected, the current 
frame is said to be characterized by a High Pixel Activity 
(HPA). In this case a highly accurate algorithm is required: 
therefore, spatial sub-sampling is disabled, parameters update 
rate is increased and, if required, the number of Gaussian 
distributions is restored. These actions are done even if buffer 
occupancy increases; only when iovflB is reached, parameters 
are again modified to speed up the algorithm.  
B. Global Level Adaptation (GLA) 
Let us suppose that the ith processing task is working with a 
processing time very close to ipTmin and buffer occupancy 
near iovflB . Algorithm parameters cannot be further modified 
since this would dramatically decrease the background model 
quality and, therefore, the detection accuracy: then a global 
adaptive strategy is required. The processing task that is using 
more processing resources is forced to reduce its computation 
cost (i.e. enabling subsampling mode). Now, the ith processing 
task can use these new available processing resources. 
IV. EXPERIMENTAL RESULTS 
The system has been intensively tested in an indoor complex 
environment with three cameras connected to the PU. Each 
camera provides a stream of color images of 1032x778 pixels 
at 20 fps. The used detection algorithm is a variation of [2] 
presented by Power and Schoones in [4], and applied to the 
YCbCr space. Fig. 2 shows the results of the two level 
adaptation strategy: (a) shows an example of LLA where 
buffer occupancy (green line) and pixels activity (blue line) are 
reported. As it can be noticed, buffer occupancy is bounded 
between iovflB and 
i
uflB
 (dashed lines) and, while LPA is found, 
it maintains a value very close to iuflB . When HPA is identified 
(frame 115), background model accuracy is ensured assigning 
more computational resources to the detection process (thus 
increasing buffer occupancy). As expected, the LLA strategy 
guarantees an efficient utilization of computational resources 
depending on the scene activity.  Fig. 2(b) shows an example 
of GLA: when the buffer occupancy of camera 0 (black line) 
exceeds iovflB , and the LLA cannot further reduce the 
processing time without compromising the detection accuracy 
(it is already working at ipTmin ), the GLA reduces the 
computational requirements of the camera 1 processing 
module (blue line). The new available processing resources are 
given to camera 0 processing module, thus reducing its buffer 
occupancy. Fig. 3 shows, for an acquired original frame in (a), 
the detection result using the original algorithm (full 
processing requirements) in (b), and that when using the 
adaptive algorithm: moving object is clearly detected with only 
a moderate loss in its segmentation quality.  
  
 
Fig. 2.  Two level adaptation strategy example: (a) Buffer occupancy (green 
line) and scene activity (blue line); (b) buffer occupancy of the three buffers 
of the system.                                                                 
 
 
Fig. 3.  Frame detail (a), object detected with the original algorithm (b), 
object detected with the adaptive system (c). 
V. CONCLUSIONS AND FUTURE WORKS 
In this work we present an adaptive multi-camera system for 
object detection based on a two level adaptation strategy that 
allows to dynamically tuning the processing requirements as a 
function of the scene activity and the available processing 
resources without affecting the algorithm accuracy. Next steps 
will address its integration with a real time 3D tracking system. 
REFERENCES 
[1] M. Piccardi. Background subtraction techniques: a review. In IEEE, 
International Conference on Systems, Man and Cybernetics, vol. 4, pp 
3099-3104. October 2004. 
[2] C. Stauffer, and W. E. L. Grimson, “Adaptive background mixture 
models for real-time tracking,” IEEE Computer Society Conference on 
Vision and Pattern Recognition, vol.2, pp. 2246-2252, August 1999. 
[3] T. Bouwmans, F. El Baf, B. Vachon,  “Background Modeling using 
Mixture of Gaussian for Foreground Detection,” Recent Patent on 
Computer Science, vol.1, 2008. 
[4] P. Power, J. Schoones “Understanding background mixture models for 
foreground segmentation”, Imaging and Vision Computing NewZeland 
2002, November 2002. 
[5] A. Shimada, D. Arita, R. Taniguchi, “Dynamic control of adaptive 
mixture-of-gaussian background model”. AVSS 2006. November 2006. 
798


Adaptive Multi-Camera System for Real Time Object Detection

Archivo Digital UPM

Adaptive background mixture models for real-time tracking,”

Background Modeling using Mixture of Gaussian for Foreground Detection,” Recent Patent on Computer Science,

Background subtraction techniques: a review.

Dynamic control of adaptive mixture-of-gaussian background model”. AVSS

Schoones “Understanding background mixture models for foreground segmentation”,

Adaptive Multi-Camera System for Real Time Object Detection

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