Background:
Moving object detection in dynamic environment video is more complex
than the static environment videos. In this paper, moving objects in video sequences are detected
and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS)
classifier approach. The proposed moving object detection methodology is tested on different
video sequences in both indoor and outdoor environments.
Methods:
This proposed methodology consists of background subtraction and classification
modules. The absolute difference image is constructed in background subtraction module. The
features are extracted from this difference image and these extracted features are trained and
classified using ANFIS classification module.
Results:
The proposed moving object detection methodology is analyzed in terms of Accuracy,
Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation
methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and
2.4 GHz for evaluating the performance during moving object segmentation. At present, some
moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving
object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4
GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the
performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100
sequence). Table 2 shows the performance evaluation of proposed moving object detection on
CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor
1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for
meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as
depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is
68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain
sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8
seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average
elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from
Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU
processor increases.
Conclusion:
In this paper, moving object is detected and segmented using ANFIS classifier. The
proposed method initially segments the background image and then features are extracted from the
threshold image. These features are trained and classified using ANFIS classification method. The
proposed moving object detection method is tested on different video sequences which are obtained
from different indoor and outdoor environments. The performance of the proposed moving
object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average
Accuracy, Precision and F-measure.
Accurate moving object segmentation in unconstraint videos based on robust seed pixels selection
