This paper proposes an efficient scheme for detecting different object classes in an imaging sensor network. The object detection system detects all the instances of objects (for which the classifier was trained) in the given image, regardless of their scales and locations. Therefore, the image can be thus seen as a set of sub-windows that are to be evaluated by the detector. The detector selects those sub-windows that contain the instances of the objects trained. The traditional approach for multiclass object detection is to use different independent classifiers to the image, at multiple locations and scales. This can be slow and requires a lot of training data. To achieve a fast and robust implementation, shared features are used. In the existing schemes, part-based models have been used for evaluating the object features, so these features being more class-specific cannot share more information among different classes. Hence in this paper, rectangular features called Haar-like features which are more generic is used and thus more number of features can be shared. The proposed scheme uses Joint-Boosting algorithm for training the multiclass object classifier. The benefits of illumination normalisation or variance normalisation technique used to neutralise the effect of changing lighting conditions are explored. Though the proposed scheme is validated for car and pedestrian classes, the training and detection techniques used in this scheme can be generalised for any object class. © 2011 IEEE.