The objective of object recognition algorithms in computer vision is to quantify the presence or absence of a certain class of objects, for e.g.: bicycles, cars, people, etc. which is highly useful in traffic estimation applications. Sparse signal models and dictionary learning techniques can be utilized to not only classify images as belonging to one class or another, but also to detect the case when two or more of these classes co-occur with the help of augmented dictionaries. We present results comparing the classification accuracy when different image classes occur together. Practical scenarios where such an approach can be applied include forms of intrusion detection i.e., where an object of class B should not co-occur with objects of class A. An example is when there are bicyclists riding on prohibited sidewalks, or a person is trespassing a hazardous area. Mixed class detection in terms of determining semantic content can be performed in a global manner on downscaled versions of images or thumbnails. However to accurately classify an image as belonging to one class or the other, we resort to higher resolution images and localized content examination. With the help of blob tracking we can use this classification method to count objects in traffic videos. The method of feature extraction illustrated in this paper is highly suited to images obtained in practical cases, which are usually of poor quality and lack enough texture for the popular gradient based methods to produce adequate feature points. We demonstrate that by training different types of dictionaries appropriately, we can perform various tasks required for traffic monitoring.