Rapidly finding animals is useful in many fields but this post will focus on agriculture. Through cooperation with Purdue's Beef Unit myself and two undergraduates were able to acquire data-sets of cattle in a field. In this data-set there were two populations of cattle that I was interested in, brown and black. I began by creating spectral databases following the rules that I described in my last post, large numbers of individual color files instead of color ranges or single files of individual colors. The databases I created can be seen in figure 1.
Figure 1: Color databases
Figure 2 and 3 show examples of the data-set collected.
Figure 2: Cattle data-set sample 1
Figure 3: Cattle data-set sample 2
Looking at figures 2 and 3 it is clear that most of the cattle are black, and that there are two main groupings. The grouping near the giant puddle and the grouping in separate pens. Within these groupings there are separate groups. The puddle group has the cattle in the puddle and those trailing away from the puddle. The pen group has two groups somewhat near each other in the pens. Only one grouping, the puddle grouping, has a brown cow. I performed 3 tests looking for brown cattle in the data-set manipulating the minimum number of pixels allowed for a positive hit. I began by looking with a minimum pixel of 1 then 2 then 4 and determined that using a minimum pixel of 1 was most effective. Figures 4 through 7 show examples of these results.
Figure 4: Brown Cow example 1
Figure 5: Close up of example 1
Figure 6: Brown Cow example 2
Figure 7: Close up of example 2
In both of these examples the desired animal was found. In figure 5 the close up shows that Loc8 found multiple positive hits on the animal while figure 7 only shows one positive hit. While this is fairly interesting there is no real functional difference, and the software was able to provide the location for the animal in both cases. This test shows the capability of Loc8 to find and Geo-locate a specific animal out of a group of animals. This was possible because the animal had different coloration patterns from the group, but this has potential for future investigation.
Locating one unique animal in a group is useful, but will the software be able to find the location of the larger groups? The biggest challenge with finding the black cattle, or any black object, is preventing shadows from being located instead of the desired objects. Figures 8 and 9 show examples of the puddle group of the cattle.
Figure 8: Puddle group example 1
Figure 9: Puddle group example 2
Figure 8 shows that most of the cattle trailing towards the puddle have been located using this method, and that the cattle in the puddle are all encompassed in a single circle. Figure 9 does not alert to any of the cattle leading up to the puddle, but does alert to the puddle cattle separately. This is interesting because sometimes the groups of cattle are located as a group and sometimes they are located as individuals. I am unsure of why this is, but both methods do provide the location of the animal groups. Figure 10 below shows how Loc8 performed in finding the cattle in separate pens.
Figure 10: Cattle in pens
Figure 10 shows most of the cattle being found in five groups, and one false positive of a shadow. As I stated earlier, when looking for black objects shadows often show up as false positives. This data-set showed surprisingly few false positives from shadow, and I believe this is because of the many shades of black used for the search.
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