Vehicle Make / Model Determination
It’s hard to quantify the amount of requests we’ve received over the years where investigators have asked us to resolve a few pixels or blocks into a license plate or other identifying item. If, at the Content Triage step, nominal resolution isn’t sufficient to answer the question, we say so.
When the data supports no conclusion, we try to quantify why. We’ll usually note the nominal resolution and / or the particular defect that may be getting in the way – blur, obstruction, etc. What we don’t do is equivocate. Quite the opposite, we try to be very specific as to why the question can’t be answered. In this way, there is no ambiguity in my conclusion(s).
Additionally, whomever is responsible for the source of the evidence will have insight as to potential improvements to the situation. For example, if the question is “what is license plate,” and the camera is positioned to monitor a parking lot, then the person / company responsible for the security infrastructure can be alerted to the potential need for additional coverage in the area of interest. This could mean additional cameras, or a change in lensing, or …
What does this have to do with vehicle make / model determination?
Examine this article about a homicide case in Southern California. “Expert says Merritt truck ‘cannot be excluded’ as vehicle on McStay neighborhood video.” Really? “Cannot be excluded?” What does, “cannot be excluded” mean?
At issue is a 2000 Chevy work truck, similar to the one below. Chevy makes some of the most popular trucks in the US, second only to Ford. This case takes place in Southern California, home to more than 20m people from Kern to San Diego counties.
“Cannot be excluded” is not a conclusion, it’s an equivocation. Search CarFax.com for used Chevy Silverado 3500 HD trucks for sale within a 100 mile radius of Fallbrook, CA (92028). We did. We found 49 trucks for sale on that site. 49 trucks that “cannot be excluded” … AutoTrader listed 277 trucks for sale. 277 more trucks that “cannot be excluded” …
All of this requires us to ask a question, is the goal of a comparative analysis or a make / model determination to “exclude” or to “include?” How would you know if you’ve never taken a course in comparative analysis? Perhaps we can start with the SWGDE Best Practices for Photographic Comparison for All Disciplines. Version: 1.1 (July 18, 2017) (link):
- Class Characteristic – A feature of an object that is common to a group of objects.
- Individualizing Characteristic – A feature of an object that contributes to differentiating that object from others of its class.
- 5.2 Examine the photographs to determine if they are sufficient quality to complete an examination, and if the quality will have an effect on the degree to which an examination can be completed. Specific disciplines should define quality criteria, when possible, and how a failure to meet the specified quality criterion will impact results. (This may apply to a portion of the image, or the image as a whole.)
- 5.2.1 If the specified quality criteria are not met, determine if it is possible to obtain additional images. If the specified quality criteria are not met, and additional images cannot be obtained, this may preclude the examiner from conducting an examination, or the results of the examination may be limited.
- 5.3 Enhance images as necessary. Refer to ASTM Guide E2825 for Forensic Digital Image Processing.
These steps are the essence of the Content Triage step in the workflow – does the analyst have enough nominal resolution to continue processing and reach a conclusion?
But, there is more to this process than just a comparison of a “known” and an “unknown.” How does one go from “unknown” to a “known” for a comparison? How do you “know” what is “known?” First, you must attempt a Vehicle Make / Model Determination of the vehicle in the CCTV footage.
For a Vehicle Make / Model Determination, the SWGDE Vehicle Make/Model Comparison Form. Version: 1.0 (July 11, 2018) (source) is quite helpful.
How many features are shared between model years in a specific manufacturer’s product line? Class Characteristics can help get you to “truck,” then to “work truck” (presence of exterior cargo containers not typically present in a basic pickup truck), then to “make” based on shapes and positions of features of the items found in the Comparison form. The form can be used to document your findings.
You may get to Make, but getting to Model in low resolution images and video can be frustrating. What’s the difference between a Chevy Silverado 1500, 1500LD, 2500HD, 3500HD? There are more than 10 trim variations of the 1500 series alone. What’s the difference between a 2500HD and a 3500HD?
After you’ve documented your process of going from “object” to “work truck” to a specific model of work truck, how do you move beyond class, to make, to model, to year, to a specific truck? Remember, an Individualizing Characteristic is a feature of an object that contributes to differentiating that object from others of its class. Before you say “headlight spread pattern,” please know that there is no valid research supporting “headlight spread pattern” as an individualizing characteristic – NONE. We are aware of cases where this technique has been used, but rhetoric is not science. Many jurisdictions, such as California and Georgia, will allow just about everything in at trial, so not having one’s testimony excluded at trial is not proof of anything scientific.
Taking your CCTV footage, you’ve made your make / model / year determination using the SWGDE’s form. Now, how do you move to an individual truck?
This is where basic statistics and inferential reasoning are quite necessary. Do you have sufficient nominal resolution to pick out identifying characteristics in the footage? If not, you’re done. The data supports no conclusion as to individualization.
But assuming that you do, how do you work scientifically and as bias free as possible? Unpack the biasing information that you received from your “client” and design an experiment. This is the ethical thing to do.
In the US, given our Constitutional provisions that the accused are innocent until proven guilty, it is for the prosecution to prove guilt. Thus, the staring point for your experiment is that the truck in question is not a match. With sufficient nominal resolution, you set about to prove that there is a match. If you can’t, there is no match as far as you’re concerned. Remember, the comparative analysis or the make / model determination should not be influenced by any other factors or items of evidence.
In designing the experiment, you’ll need a sample set of images. You see, a simple “match / no match” comparison needs an adequate sample. It perverts the course of justice to simply attempt the comparison on the accused’s vehicle. We don’t do witness ID line-ups with just the suspect. Neither should anyone attempt a comparison with just a single “unknown” image – the accused’s. Yes, we do use this specific provision of English Common Law to explain the problem here. Perverting the Course of Justice can be any of three acts, fabricating or disposing of evidence, intimidating or threatening a witness or juror, intimidating or threatening a judge. In this case, one Perverts the Course of Justice when one fabricates a conclusion (scientific evidence) where none is possible.
Back to the experiment. How many “unknown” images would you need to approach 99% confidence in your results, thus assisting the course of justice? Answer = 52. How did we come up with 52?
Exact – Generic binomial test
Analysis: A priori: compute required sample size
- Input: Tail(s) = One
- Proportion p2 = 0.8
- α err prob = 0.01
- Power (1-β err prob) = .99
- Proportion p1 = 0.5
- Output: Lower critical N = 35.0000000
- Upper critical N = 35.0000000
- Total sample size = 52
- Actual power = 0.9901396
- Actual α = 0.008766618
A generic binomial test is similar to the flip of a coin – only two possible outcomes, heads / tails or match / no match. It’s the simplest test to perform.
The error probability is your chance of being wrong. At 52 test images, you’ve got a 1 in 100 chance of being wrong (.99). As you move below 15 test images, you have a greater chance of being wrong than being right (see the graphic above). With a sample size of 1, you’re likely more accurate tossing a coin.
The 52 samples help us to get to make / model / year. You may chose to refresh those samples with new ones to perform a “blind comparison,” and attempt to “include” the suspect’s vehicle in your findings. To do this, you’d need the specific description of the “known” vehicle that makes it unique vs the others in the sample. But you’ll need to do this in a way that prevents the analyst from knowing which to choose.
If we were performing a make / model / year determination, and then a comparison, we would note any errors or limitations in our report. If the data supported no conclusion, or if the limitations in the data prevented us from arriving at a determination, we would note that the data supported no conclusion. If we were able to make a determination, we would have noted our process and how we arrived at the conclusion (in a reliable, valid, and reproducible fashion).
The problem with the reporting of the case is the “cannot be excluded” portion is in the headline. One has to read deeper into the article to find, “… [the analyst] denied (that his conclusions may have been formed to fit the bias of the prosecution, who was paying him…), and reminded [the attorney] more than once that he had not identified the truck specifically as Merritt’s…”
Which requires another question be asked, if the analyst had not identified the vehicle, what was he doing there in testimony?
“Among the items that helped to reach the conclusion that the vehicle was “consistent” with Merritt’s truck was a glint caught by the video that matched the position of a latch on a passenger-side storage box toward the rear of the truck, said [the analyst],who uses 3D imagery.”
Here we move from “cannot be excluded” to “consistent with,” another equivocation. How does one not identify a vehicle, but find that said unknown vehicle is “consistent with” the “known” vehicle? This is the problem with Demonstrative Comparisons. When you place a single “known” against a single “unknown” in a demonstrative exhibit, you are making a biased choice as to what to include in your exhibit – thus you have concluded.
Back to the demonstrative. What is it about the latch on the side of the truck that is unique? Won’t all work trucks of this type have latches on their cargo containers? Why is this one so special that it can only be found on the accused’s truck? Of these questions, the article does not give an answer.
“I’m not saying that this your client’s vehicle,” [the analyst] repeated. “All I am saying is that the vehicle in question is consistent with my report, and if there is another vehicle that looks similar, that is possible.” How about at least 326 vehicles found on just two used car web sites?
If you’d like to explore these topics in depth, we invite you to sign up for any one (or all) of our upcoming training sessions. Our Statistics for Forensic Analysts course is offered on-line as micro learning and thus enrollment can happen at your convenience. Our other courses can be facilitated at your location or at ours, in Henderson, NV.